Delft University of Technology
FACULTY MECHANICAL, MARITIME AND MATERIALS ENGINEERING
Department Maritime and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone +31 (0)15-2782889 Fax +31 (0)15-2781397 www.mtt.tudelft.nl
Specialization: Transport Engineering and Logistics Report number: 2017.TEL.8149
Title: Applications of Internet of Things in Maritime Transport
Author: P.L. Vos
Title (in Dutch) Toepassingen van Internet der Dingen in Maritiem Transport
Assignment: literature assignment Confidential: no
Supervisor: Dr. ir. Y. Pang
Date: juni 2, 2017
TUDelft
FACULTY OF MECHANICAL, MARITIME AND MATERIALS ENGINEERINGDelft University of Technology Department of Marine and Transport Technology Mekelweg 2 2628 CD Delft the Netherlands Phone + 3 1 (0)15-2782889 Fax -t-31 (0)15-2781397 www.mtt.tudelft.nl Student: Supervisor: Specialization: Creditpoints (EC): Peter Vos Dr. ir. Y.Pang TEL 10 Assignment type: Report number: Confidential: Literature Assignment 2017.TEL.8149 No
Subiect: Application of Internet of Things in Maritime Transport
Internet of Things (loT) is a novel paradigm that is rapidly gaining ground in modern remote telecommunication and information integration. The fundamental of this concept is the pervasive presence around us of a variety of things or objects - such as Radio-Frequency IDentification (RFID) tags, sensors, actuators, mobile phones, etc. - which, through unique addressing schemes, are able to interact with each other and cooperate with their neighbors to reach common goals. This enables key benefits such as better performance, improved reliability and safety.
Nowadays the application of loT becomes growing in maritime transport. This literature assignment aims to explore the state of the art development of loT and the application in the specific transport and logistic field. The survey of this assignment will cover the following:
• to review the general concepts of loT
9 to investigate the characteristics, general architectures, basic interaction protocols and
intelligent abilities of loT in the maritime domain
• to summarize the methodologies for designing loT systems in the marine sector » to explore the applications of loT technology in the marine sector
• to demonstrate the intelligent control for the marine sector by means of loT systems This report should be arranged in such a way that all data is structurally presented in graphs, tables, and lists with belonging descriptions and explanations in text.
The report should comply with the guidelines of the section. Details can be found on the website. The mentor.
© Copyright 2017 by Peter L. Vos
PREFACE
This literature study is written for the master Transport Engineering and Logistics at the TU Delft. Internet of Things is a hot topic in the field of Transport and Logistics and its potential is broadly accepted. In this report the focus will lie on Maritime Transport, and the advantages and challenges to implement the Internet of Things in it. I would like to thank Dr. Ir. Yusong Pang for his excellent guidance during this project. It was a challenging and really informative study that learned me a lot of intelligent control systems in the transport sector, and in particular IoT in MT.
Peter L. Vos Delft, 2017
TABLE OF CONTENTS
LIST OF TABLES . . . vi
LIST OF FIGURES . . . vii
Chapter 1 Introduction . . . . 1
1.1 General Introduction . . . 1
1.2 Scope and Goal of the Report . . . 2
1.3 Structure of the Report . . . 3
Chapter 2 Internet of Things . . . . 5
2.1 Definition of the Internet of Things . . . 5
2.2 The IoT Paradigms . . . 7
2.2.1 The "Things Oriented" Vision . . . 7
2.2.2 The "Internet Oriented" Vision . . . 8
2.2.3 The "Semantic Oriented" Vision . . . 9
2.3 Development of the IoT . . . 9
2.4 Forecasting the future of IoT . . . 10
2.5 Architecture of IoT Systems . . . 12
2.6 IoT Technologies . . . 13
2.6.1 Data Acquisition . . . 14
2.6.2 Identification and Tracking . . . 16
2.6.3 Communication and Networking . . . 19
2.6.4 Middleware . . . 21
2.6.5 Data Storage and Analytics . . . 21
2.6.6 Applications . . . 22
2.7 Internet Protocol Suite . . . 23
2.7.1 IoT Protocols . . . 25
2.8 Big Data and Cloud Computing . . . 27
Chapter 3 Maritime Transport . . . 29
3.1 Definition of Maritime Transport . . . 29
3.2 Which Parties and Actors are involved? . . . 31
3.3 Opportunities . . . 33
Chapter 4 IoT in MT . . . 35
4.1 Possibilities for IoT in MT . . . 35
4.1.1 Route Optimization . . . 36
4.1.2 Asset Tracking . . . 37
4.1.3 Equipment Monitoring . . . 38
4.2 Ship Connectivity . . . 41
4.2.1 Available Network Technologies . . . 41
4.2.2 Developing Network Technologies . . . 47
Chapter 5 Applications of IoT in MT . . . 55
5.1 IIoT for the Maritime Industry in Northwestern Norway . . . 55
5.2 TeamTec AS in Norway . . . 57
5.3 TRITON: High Speed Maritime Mesh Networks . . . 60
Chapter 6 Conclusion . . . 65
Chapter 7 Discussion . . . 67
7.1 Lack of IoT in MT . . . 67
7.2 Trends . . . 68
LIST OF TABLES
Table 2.1 Comparison between RFID systems, WSN, and RFID sensor networks[Atzori
et al., 2010]. . . 19
Table 2.2 A Cisco View on IoT Protocols[Duffy, 2017] . . . 25
Table 3.1 Systems of a ship[Kristiansen, 2005]. . . 32
Table 3.2 Functions of a ship[Kristiansen, 2005]. . . 32
Table 4.1 IMO definition of sea area[Yun et al., 2012] . . . 42
Table 4.2 Characteristics of Networking Technologies for Marine Internet edited by Peter Vos[Jiang, 2015a] . . . 49
Table 5.1 Through-life Engineering Services identified for Improvement[von Stietencron et al., 2017]. . . 58
Table 5.2 Summary of general Objectives identified[von Stietencron et al., 2017]. . . 58
Table 5.3 Summary of specific Objectives identified for ship data acquisition[von Sti-etencron et al., 2017]. . . 58
LIST OF FIGURES
Figure 1.1 Transport forecast[Port vision 2030, Port of Rotterdam] . . . 2
Figure 2.1 IoT paradigm as a result of the convergence of different visions[Atzori et al., 2010]. . . 8
Figure 2.2 Hype cycle for emerging technologies, 2011[gartner.com, 2011]. . . 10
Figure 2.3 Global IoT forecasts modified for data from 2017 by Peter Vos[IoT Analytics, 2014]. . . 12
Figure 2.4 The Internet of Things from an embedded systems point of view[micrium.com, 2015] . . . 14
Figure 2.5 IoT technologies[Lodewijks et al., 2016a]. . . 14
Figure 2.6 IoT sensors and actuators[Research, 2016]. . . 15
Figure 2.7 Typical multi-hop WSN architecture[Wireless Sensor Networks, 2017] . . . 18
Figure 2.8 Gateway architecture[micrium.com, 2015] . . . 21
Figure 2.9 SOA-based architecture for the IoT middleware[Atzori et al., 2010]. . . 22
Figure 2.10 TCP/IP stack reference model [micrium.com, 2015] . . . 24
Figure 2.11 Comparison of web and IoT protocol stacks[Shelby, 2013] . . . 27
Figure 2.12 The big data value chain[Spekreijse, 2016] . . . 28
Figure 3.1 Global freight transport in trillions of tonne-kilometers[OECD, 2017]. . . 30
Figure 4.1 Remote container management network[Churchill, 2016] . . . 38
Figure 4.2 Condition monitoring process[DNV GL, 2014a] . . . 39
Figure 4.3 Frequency bands relevant for maritime communications[Lag, 2015] . . . 45
Figure 4.4 Communication broker solution from MCP[mcp.com, 2017] . . . 46
Figure 4.5 A hybrid networking structure for marine Internet[Jiang, 2015a] . . . 49
Figure 4.6 Three types of signal propagation manners, i.e., LoS, shadowing and reflection propagation[Wang et al., 2011] . . . 51
Figure 5.1 Conceptual 2-layer BDA-IIoT framework for OSV[Wang et al., 2016] . . . 57
Figure 5.2 Block diagram of chosen methodology[von Stietencron et al., 2017] . . . 59
Figure 5.3 The HLA of a multi-hop Wireless mesh network for maritime communications [Zhou et al., 2013]. . . 61
Figure 5.4 General topology for maritime wireless mesh networks[Pathmasuntharam et al., 2008] . . . 63
Figure 5.5 Average length (hop count) of a shortest route formed using Dijkstra’s algo-rithm. Effect of channel quality, p given when participate q= 1. [Pathmasun-tharam et al., 2008] . . . 63
Figure 5.6 Average length of a shortest route formed using Dijkstra’s algorithm. Effect of participation rate, q when channel quality p= 1. [Pathmasuntharam et al., 2008] . . . 63
ACRONYMS
BD Big Data
BDA Big Data Analytics
CC Cloud Computing
ERP Enterprise Resource Planning
FPGA Field-Programmable Gate Array
HAPs High Altitude Platforms HLA High Level Architecture
ICT Information Communication Technologies IMO International Maritime Organization IoT Internet of Things
IP Internet Protocol
IPSO Internet Protocol for Smart Objects ITU International Telecommunication Union
Iø Internet ø
LF Low Frequencies
LoS Line of Sight
MOS Motorways of the Sea
MRO Maintenance, Repair and Overhaul
MS Maritime Sector
MSS Mobile Satellite Systems
MT Maritime Transport
NFC Near Field Communications
OSVs Offshore Support Vessels
RCM Reliability-Centered Maintenance RCM Remote Container Management RFID Radio-Frequency IDentification RSN RFID Sensor Networks
SLA Service-Level Agreements
SO Smart Objects
SOA Service Oriented Architecture
TCP Transmission Control Protocol TES Through-life Engineering Services
TRITON TRI-media Telematic Oceanographic Network
UHF Ultra High Frequencies
UN United Nations
VSAT Very Small Aperture Terminal
VTMR Vessel Traffic Monitoring & Reporting
WANETs Wireless ad hoc Networks
WiMAX Worldwide Interoperability for Microwave Ac-cess
WISEPORT WIrelessbroadband-access for SEaPORT WISP Wireless Identification and Sensing Platforms WPAN Wireless Personal Area Networks
WSAN Wireless Sensor and Actuator Networks WSN Wireless Sensor Networks
CHAPTER
1
INTRODUCTION
1.1
General Introduction
The global freight shows an exponential growth over the last decades, and forecasts are that this growth will continue for the next years. Figure 1.1 shows four different forecast scenarios for the port of Rotterdam. Four different scenarios are sketched from low economic growth to a global growth of the economy at the rate its growing right now. And all show a growth of the amount of weight that will be transported in the port of Rotterdam for 2030.
According to a new market report published by Persistence Market Research, the global market for shipping containers is estimated to reach a value of 8.29 Bn USD by the end of 2015, and is expected to expand at an annual growth rate of 5.6% from 2015 to 2021, to reach a market value of 11.47 Bn USD by 2021[Persistence Market Research, 2015]. In a market that big, efficiency becomes the name of the game. As a result of the revolutions within sensors, communication, and data analytics, there are now "connected vessels", with communication infrastructures that enable the implementation of a range of new applications based on the data now available on board[Lag, 2015]. These vessels are connected with each other and the rest of the world over the Internet, enabling the Internet of Things (IoT) in Maritime Transport (MT).
Figure 1.1 Transport forecast[Port vision 2030, Port of Rotterdam]
telecommunications[Atzori et al., 2010]. The basic idea of this concept is the pervasive presence around us of a variety of things or objects - such as Radio-Frequency IDentification (RFID) tags, sensors, actuators, mobile phones, etc. - which, through unique addressing schemes, are able to interact with each other and cooperate with their neighbors to reach common goals[Giusto et al., 2010]. To use the full potential of IoT in MT, ship connectivity should be improved to obtain sufficient and low-cost broadband Internet at sea. Enabling key benefits such as better performance, improved reliability and safety. The era of IoT is upon us and will make a dramatic impact on the maritime transport as we know it today.
1.2
Scope and Goal of the Report
Since both the IoT and MT are huge topics alone, the scope for both topics is determined. For IoT the focus will lie on the technologies and architecture of IoT systems and what advantages they have. Big data and cloud computing are topics which always come in combination with IoT, but will only briefly mentioned in this paper. In general MT consists of the entire value chain related to
maritime transport, including the logistics before and after the shipping phase. In this paper only the shipping phase will be analyzed, and only briefly how it connects with the other systems before and after this phase.
The goal of this literature study is to give an overview of IoT in MT, the current techniques, its benefits, and challenges, with the aim of making maritime transport smarter, safer, and more efficient.
1.3
Structure of the Report
The first chapter is this short introduction to the topic and the goal of this study. The second chapter is about the Internet of Things (IoT), a general explanation of it is given, as well as different techniques and the architecture of such a system is explained. In chapter three the description of Maritime Transport is given, the parties involved, and the current technologies. Chapter four will then tell about the possibilities for IoT in MT, and the main challenges it faces. Chapter five gives some applications where IoT is already implemented in MT. A discussion about the trends, and further research is given in chapter 6, and the final chapter will conclude this paper.
CHAPTER
2
INTERNET OF THINGS
To properly understand the Internet of Things (IoT), the use of it, and its advantages in the maritime sector, a general understanding of the concept of IoT is necessary. Therefore, this chapter gives an introduction to the IoT and IoT systems. Firstly the general concept of the IoT is explained, and its different visions. In the second part, a short time-line of how the IoT developed through the years, and a forecast for the future is given. Lastly, the different IoT techniques, general architectures, interaction protocols and intelligent abilities are discussed.
2.1
Definition of the Internet of Things
The IoT is a novel paradigm that is rapidly gaining ground in the scenario of modern wireless telecommunications[Atzori et al., 2010]. The basic idea of this concept is the pervasive presence around us of a variety of things or objects - such as Radio-Frequency IDentification (RFID) tags, sensors, actuators, mobile phones, etc. - which, through unique addressing schemes, are able to interact with each other and cooperate with their neighbors to reach common goals[Giusto et al., 2010].
Many different definitions can be found in literature and an interested reader might experience a real difficulty in understanding what IoT really means, which basic ideas stand behind this concept,
and which social, economical and technical implications the full deployment of IoT will have[Atzori et al., 2010].
Santucci[Santucci and Lange, 2008] states that the definition of the "Internet of Things" still has some fuzziness, and can have different facets depending on the perspective taken. Considering the functionality and identity as central he describes the IoT as:
"Things having identities and virtual personalities operating in smart spaces using intelligent interfaces to connect and communicate within social, environmental, and
user contexts"
A different definition, that puts the focus on the seamless integration, could be formulated as:
"Interconnected objects having an active role in what might be called the Future Internet"
The semantic origin of the expression is composed by two words and concepts: "Internet" and "Things", where "Internet" "The world-wide network of interconnected computer networks, based on a standard communication protocol, the Internet suite (TCP/IP)" [Santucci and Lange, 2008]. According to Miorandi et al. things or Smart Objects (SO) are defined as entities that:
• Have a physical embodiment and a set of associated physical features (e.g., size, shape, etc.). • Have a minimal set of communication functionalities, such as the ability to be discovered and
to accept incoming messages and reply to them.
• Possess a unique identifier.
• Are associated to at least one name and one address. The name is a human-readable descrip-tion of the object and can be used for reasoning purposes. The address is a machine-readable string that can be used to communicate to the object.
• Possess some basic computing capabilities. This can range from the ability to match an incoming message to a given footprint (as in passive RFIDs) to the ability of performing rather complex computations, including service discovery and network management tasks.
• May possess means to sense physical phenomena (e.g., temperature, light, electromagnetic radiation level) or to trigger actions having an effect on the physical reality (actuators).
Santucci[Santucci and Lange, 2008], therefore, states that the "Internet of Things" means: "a world-wide network of interconnected objects uniquely addressable, based on
2.2
The IoT Paradigms
In a survey about the IoT by Atzori et al., the term IoT is defined in a number of different ways:
• Things or objects, which through addressing schemes interact with each other and cooperate with their neighbors to reach common goals[Atzori et al., 2010].
• Interconnecting physical objects with computing and communication capabilities across a wide range of services and technologies[Miorandi et al., 2012].
• Interconnection of sensing and actuating devices providing the ability to share information across platforms through a unified framework... with Cloud computing as the unifying frame-work[Gubbi et al., 2013].
As identified by Atzori et al., these definitions can be realized in three paradigms. The first definition comes from a networking perspective, the second uses physical attributes as the base for the loT definition and the third definition emphasizes the use of platforms and the cloud.
In Figure 2.1 the three paradigms can be seen, and the main technologies, concepts, and stan-dards are placed to the IoT visions they characterize best. This Figure clearly shows that the IoT can only be useful in application domains where these three visions intersect[Gubbi et al., 2013].
2.2.1 The "Things Oriented" Vision
The "Things Oriented" vision sees the IoT as a network of smart physical or virtual object with extended Internet technologies and at the same time with a set of technologies that realize this. The focus lies on the physical embodiment of IoT[Lodewijks et al., 2016a]. According to M. Presser [Presser and Gluhak, 2009], RFID still stands at the forefront of the technologies driving the vision of IoT. The consequence of this is the maturity of RFID technology, low cost, and strong support from the business community. However, they state that a wide portfolio of device, network, and service technologies will eventually build up the IoT. Near Field Communications (NFC) and Wireless Sensor and Actuator Networks (WSAN) together with RFID are recognized as "the atomic components that will link the real world with the digital world"[Atzori et al., 2010]. It is also worth recalling that major projects are being carried out with the aim of developing relevant platforms, such as the Wireless Identification and Sensing Platforms (WISP) project. In the "Things Oriented" vision not only Presser[Presser and Gluhak, 2009] talks about something going beyond RFID. Other relevant institutions like the United Nations (UN) and International Telecommunication Union (ITU) predict the advent of IoT and the concept that IoT has primarily to be focused on the "Things" and that the
Figure 2.1 IoT paradigm as a result of the convergence of different visions[Atzori et al., 2010].
road to its full deployment has to start from the augmentation in the Things’ intelligence[Sterling, 2005] [ITU, 2005].
2.2.2 The "Internet Oriented" Vision
The "Internet Oriented" vision incorporates the requirement for a standardized communication architecture that should allow the IoT to become widespread[Lodewijks et al., 2016a]. As seen in Figure 2.1, multiple network architectures are possible like Internet Protocol for Smart Objects (IPSO) and Internet ø (Iø). Both use a similar approach of reducing the complexity of the IP stack to achieve a protocol designed to route "IP over anything"[Dunkels and Vasseur, 2008] [Gershenfeld et al., 2004]. IPSO uses Internet Protocol (IP) stack, which is a light protocol that already connects a huge amount of communicating devices and runs on tiny and battery operated embedded devices. This gives IP all the qualities to make IoT a reality, and by incorporating IEEE 802.15.4 into the IP architecture, in the view of 6LoWPAN[Hui et al., 2009], and through a wise IP adaptation, the IoT paradigm will be automatically enabled. According to both the IPSO and Internet ø approaches, the IoT will be deployed by means of a sort of simplification of the current IP to adapt it to any object and make those objects addressable and reachable from any location[Atzori et al., 2010]. In section 2.6 the technology behind this will be further explained.
2.2.3 The "Semantic Oriented" Vision
The "Semantic Oriented" vision deals with representing, storing, interconnecting, searching and organizing information generated by the IoT[Lodewijks et al., 2016a]. The challenge is the translation of the collected raw data into useful information. In this context, semantic technologies could play a key role. In fact, these can exploit appropriate modeling solutions for things description, reasoning over data generated by IoT, semantic execution environments and architectures that accommodate IoT requirements and scalable storing and communication infrastructure[Toma et al., 2009].
2.3
Development of the IoT
The concept of a network of smart devices was discussed as early as 1926 by Nikola Tesla, who in an interview with Colliers magazine said:
"When wireless is perfectly applied the whole earth will be converted into a huge brain, which in fact it is, all things being particles of a real and rhythmic whole[Kennedy, 1926]."
Of course, this was long before the Internet was invented, but it shows the idea of connected devices has been around long before the actual Internet as we know it now existed. The first machine connected to the Internet was a modified Coke machine at Carnegie Mellon University in 1982 [University, 2014], able to report its inventory and whether newly loaded drinks were cold [Palermo, 2014]. In the next decades, many machines and devices would follow. The actual term "Internet of Things" was coined by Kevin Ashton in 1999 during his work at Procter&Gamble. Ashton who was working in supply chain optimization, wanted to attract senior management’s attention to a new exciting technology called RFID. Because the Internet was the hottest new trend in 1999 and because it somehow made sense, he called his presentation "Internet of Things"[Ashton, 2009] [Lueth, 2014].
According to Cisco Internet Business Solutions Group (IBSG)[cisco.com, 2017], the IoT was born in between 2008 and 2009 at simply the point in time when more "things or objects" were connected to the Internet than people. Citing the growth of smartphones, tablets, PC’s, etc. the number of devices connected to the Internet was brought to 12.5 billion in 2010, while the world’s human population increased to 6.8 billion, making the number of connected devices per person more than 1 (1.84 to be exact) for the first time in history[cisco.com, 2017] [kk.org/thetechnium, 2007].
Figure 2.2 shows the Gartner Hype cycle of 2011. The Hype Cycle graphic created by Gartner [gartner.com, 2017] tracks their perspective on specific technologies and their progress through a life-cycle from "technology trigger" to "plateau of productivity". When a technology appears on this cycle it shows it has high potential to grow into a technology with a big impact. If we look further along the curve we see the mobile applications and speech recognition, which at the moment are already fully accepted in society. Back in 2011 Gartner included a new emerging phenomenon on their list: "The IoT".
Figure 2.2 Hype cycle for emerging technologies, 2011[gartner.com, 2011].
2.4
Forecasting the future of IoT
The current count of devices connected to the Internet is somewhere between Gartner’s estimate of 6.4 billion (which doesn’t include smartphones, tablets, and computers)[gartner.com, 2015], International Data Corporation’s estimate of 9.1 billion (which also excludes those devices) [busi-ness.att.com, 2014], and IHS’s estimate of 17.6 billion (with all such devices included) [postscapes.com,
2016]. The predictions show that there are many different numbers of the amount of "things" con-nected to the Internet in literature[spectrum.ieee.org, 2016] [forbes.com, 2013]. To make things even harder, the definition of a "thing" is open to some debate as there is a fuzzy line between the computer/non-computer classification, and some of the things out there that look a lot like computers; is an industrial computer in a factory a thing (maybe), how about a micro-controller in a sensor (definitely)? Another delineation is how the things are connected to the network.
This all makes forecasting the future no easy task, and there’s nothing unusual or wrong about analysts and companies revising their projections. However, IoT forecasts are especially large with significant variability among firms and over time, skewing tens of billions of units in either direction [spectrum.ieee.org, 2016]. But all forecast have one thing in common: IoT is growing, and it’s growing rapidly.
The number of Connected Devices
Figure 2.3 gives an indication of the growth by comparing different forecasts that look at the number of connected devices (measured in billion units). The graph is made in 2014 by IoT Analytics and compares different forecasts made by different companies[IoT Analytics, 2017]. It gives a good impression of the differences in the forecasts by these companies. The data of 2014 is compared with new forecasts made in 2017, which are added to the graph in the form of the dots. This shows how much forecasts can change, and gives an indication of how good each prediction was 3 years later.
Cisco and Ericsson are the two companies that published data around the IoT market potential in connected devices. Back in 2014 both foresaw around 50 billion connected devices by 2020. Since they first made their projections, both Ericsson and Evans (Cisco) have lowered their expectations from 50 billion for 2020: Evans, who is now CTO of Stringify, says he expects to see 30 billion connected devices by then, while Ericsson Figures on 28 billion by 2021[spectrum.ieee.org, 2016]. In addition, the global and established research companies Gartner, IHS Global Insight, ABI research and IDC as well as the specialized IoT research firm Harbor Research have developed their own forecasts. As of 2017: IHS Markit projects 30.7 billion IoT devices for 2020, and Gartner expects 20.8 billion by that time (excluding smartphones, tablets, and computers). Lastly, IDC anticipates 28.1 billion (again, not counting those devices)[spectrum.ieee.org, 2016]. These new predictions are added to Figure 2.3.
As seen in the Figure, almost all forecasts have been revised in the last three years, which clearly shows how hard it is to give a good forecast.
Figure 2.3 Global IoT forecasts modified for data from 2017 by Peter Vos[IoT Analytics, 2014].
• In the next years there will be a massive increase in connected devices. Compared to other industries the growth rates are much higher, with annual rates ranging from 14% to 29%.
• The majority of connected devices in 2020 will be "Things". Today there are many connected devices that are not "Things" like i.e. smartphones, pc’s, tablets, etc. Expected is that "Things" will outgrow smartphones, tablets, and the like by far.
Where forecasters diverge:
• Back in 2014 the number of connected devices by 2020 show massive differences with low estimations of 18 billion, and high estimations of around 50 billion devices. That is a difference of 275%. With the knowledge of today, most companies forecast around 28 to 30 billion devices connected to the Internet by 2020. This can be seen in Figure 2.3, where it can be seen that the adjusted forecast converge around the 30 billion devices.
2.5
Architecture of IoT Systems
Even though the concept of the IoT has been under research for over a decade now, there are still many aspects that are not clearly defined. For example, today there is no standardized and specific architecture for the IoT[Karagiannis, 2014]. Despite this lack of common agreement, there is a well-known three-layer architecture that is generally accepted which consists of the Perception
Layer, the Network Layer and the Application Layer[Wu et al., 2010]. Wu et al. explains the tasks as follows:
• The main task of the Perception Layer is to perceive physical properties such as temperature, location, speed, etc., by various sensing devices and convert this information into digital signals which can be easily transmitted through digital communication networks and stored.
• The Application Layer stores, processes, and analyses the information received from the Network Layer.
• The Network Layer is responsible for transmitting data received from the Perception Layer to a data base, server, or processing center.
Based on the three-layer mode, an IoT system has four components[micrium.com, 2015]: 1. The Thing itself (the device)
2. The Local Network; this can include a gateway, which translates proprietary communication protocols to Internet Protocol
3. The Internet
4. Back-End Services; enterprise data systems, or PCs and mobile devices
The four components can be seen in Figure 2.4 and show the IoT from an embedded systems point of view. The four components can be seen, which together make an IoT system. The Figure is an example of a wireless sensor network that is connected to the Internet. Data from each sensor passes through the network node-to-node and an edge node acts as a gateway between the wireless sensor network and the Internet. The gateway can also perform local processing, provide local storage, and can have a user interface[micrium.com, 2015]. From the gateway the data is sent to the Internet, where back-end services will process the data to valuable information.
2.6
IoT Technologies
Atzori et al., Gubbi et al., and Lodewijks et al. further expand the three layer architecture from Wu et al. into the following disciplines:
• Data Acquisition
Figure 2.4 The Internet of Things from an embedded systems point of view[micrium.com, 2015]
• Communication and Networking
• Middleware
• Data Storage and Analytics
• Applications
Figure 2.5 IoT technologies[Lodewijks et al., 2016a].
Each discipline is necessary for the IoT to happen, and each discipline has multiple technological options which will be explained in this section.
2.6.1 Data Acquisition
As it has been often quoted from Lord Kelvin[Thomas, 1848]: "If you cannot measure it, you cannot improve it"
The key in IoT applications is the fact that objects or entities can gather data and provide information [Lodewijks et al., 2016a]. What kind of information depends entirely on the application. The main source of data is coming from sensors and actuators giving our world a digital nervous system. Location and data by GPS sensors, eyes and ears using cameras and microphones, along with sensory organs that can measure everything from pressure to temperature changes. There are many different sensors and actuators to gather all the different types of data and some examples are shown in Figure 2.6. According to Akyildiz et al. some design objectives for the sensors are energy efficiency (which is the scarcest resource in most of the scenarios involving sensor networks), scalability (the number of nodes can be very high), reliability (the network may be used to report urgent alarm events), and robustness (sensor nodes are likely to be subject to failures for several reasons)[Akyildiz et al., 2002]. The different types of sensors and the technology behind them are further described by Akyildiz et al. but are not in the scope of this paper.
2.6.2 Identification and Tracking
There are several technologies for identifying and tracking SO researched, improved and used in the IoT. Key technologies are RFID and Wireless Sensor Networks (WSN)[Atzori et al., 2010] [Gubbi et al., 2013] [Lodewijks et al., 2016a].
RFID
As described in section 2.3, Ashton used the term IoT to describe a new exciting technology: RFID. RFID systems are composed of one or more reader(s) and several RFID tags[Finkenzeller, 2003]. They enable wireless data communication that help in automatic identification of anything they are attached to acting as an electronic barcode[Welbourne et al., 2009] [Juels, 2006]. In general there are two categories of RFID tags: passive and active. The second category can be sub divided again in semi-passive, and active RFID tags[Atzori et al., 2010] [Miorandi et al., 2012].
Passive RFID
Passive RFID tags are not battery powered and use the power of the interrogation signal of the reader to communicate the ID to the reader. This is done through "energy harvesting", which means that the required energy for transmitting their ID is harvested from the query signal transmitted by a RFID reader in the proximity[Atzori et al., 2010]. Accordingly, RFID systems can be used for real-time monitoring of objects, without the need of being in Line of Sight (LoS); this allows for mapping the real world into the virtual world[Atzori et al., 2010].
From a physical point of view a RFID tag is a small microchip attached to an antenna (that is used for both receiving the reader signal and transmitting the tag ID) in a package which usually is simi-lar to an adhesive sticker[Juels, 2006]. Transmission can occur in different frequencies spanning from Ultra High Frequencies (UHF) at 860-960 MHz down to Low Frequencies (LF) at 124-135 kHz which have the lowest range[Atzori et al., 2010]. Dimensions can be very low: Hitachi has already developed a tag with dimensions 0.15 x 0.15 millimeters in size and 7.5 micrometers thick [thefutureofthings.com, 2017].
Active RFID
Active RFID tags have their own battery supply and can instantiate the communication by them-selves. There are two categories here: semi-passive, and active RFID tags. In active RFIDs the battery powers both the microchip as well as the transmission of the signal. In semi-passive RFIDs batteries power the microchip while receiving the signal from the reader (the radio is powered with the energy harvested by the reader signal)[Atzori et al., 2010]. Active RFID tags the radio coverage is the highest,
but the costs of the chip will be higher.
WSN
Active RFIDs are nearly the same as the lower end WSN nodes, only having limited processing capability and storage. The availability of efficient, low cost/power devices for remote sensing applications has developed rapidly due to technological advances. The combination of these factors has improved the viability of utilizing a sensor network consisting of a large number of intelligent sensors, enabling the collection, processing, analysis and dissemination of valuable information, gathered in a variety of environments[Akyildiz et al., 2002]. The scientific challenges that must be overcome in order to realize the enormous potential of WSNs are substantial and multidisciplinary in nature[Akyildiz et al., 2002]. The data of sensors is shared among sensor nodes and send to a centralized or distributed system for analytics. According to Gubbi et al., the components that make up the WSN monitoring network include:
• WSN hardware - Typically a node (WSN core hardware) contains sensor interfaces, processing units, transceiver units and power supply[Akyildiz et al., 2002].
• WSN communication stack - The nodes are expected to be deployed in an ad-hoc manner for most applications. Designing an appropriate topology, routing and MAC layer is critical for scalability and longevity of the deployed network. Nodes in a WSN need to communicate among themselves to transmit data in single or multi-hop to a base station[Juels, 2006]. This multi-hop technique can be seen in Figure 2.7.
• Middleware - A mechanism to combine cyber infrastructure with a Service Oriented Archi-tecture (SOA) and sensor networks to provide access to heterogeneous sensor resources in a deployment independent manner[Alemdar and Ersoy, 2010].
• Secure Data aggregation - An efficient and secure data aggregation method is required for extending the lifetime of the network as well as ensuring reliable data collected from sensors [Sang et al., 2006].
Today, most WSN networks are based on the IEEE 802.15.4 standard, which defines the physical and MAC layers for low-power, low bit rate communications in Wireless Personal Area Networks (WPAN) [IEEE802.org, 2017]. The higher layers of the protocol stack that are necessary for the integration of sensor nodes to the Internet are not included in this standard. Atzori et al. lists the most important difficulties for this integration:
Figure 2.7 Typical multi-hop WSN architecture[Wireless Sensor Networks, 2017]
• Sensor networks may consist of a very large number of nodes. This would result in obvious problems as today there is a scarce availability of IP addresses. With the upgrade from IPv4 to IPv6 this problem should be fixed, but this is a slow process (see section 2.6.3).
• The largest physical layer packet in IEEE 802.15.4 has 127 bytes; the resulting maximum frame size at the media access control layer is 102 octets, which may further decrease based on the link layer security algorithm utilized. Such sizes are too small when compared to typical IP packet sizes.
• In many scenarios sensor nodes spend a large part of their time in a sleep mode to save energy and cannot communicate during these periods. This is absolutely anomalous for IP networks.
To overcome these difficulties the WISP project is being carried out at Intel Labs, which integrates sensing technologies into passive RFID tags[alansonsample.com, 2017]. WISPs are read and powered by the RFID readers in the same way as passive RFID tags are read. This allows to built RFID Sensor Networks (RSN), which consist of small, RFID-based sensing and computing devices, and RFID readers, which are the sinks of the data generated by the sensing RFID tags and provide the power for the network operation[Buettner et al., 2008].
Table 2.1 compares the characteristics of RFID systems (RFID), wireless sensor networks (WSN), and RFID sensor networks (RSN)[Buettner et al., 2008]. As observed by Atzori et al., the major advantages of:
• RFID systems are the very small size and the very low cost. Furthermore, their lifetime is not limited by the battery duration.
• WSNs are the high radio coverage and the communication paradigm, which does not require the presence of a reader (communication is peer-to-peer whereas, it is asymmetric for the other types of systems).
• RSNs are the possibility of supporting sensing, computing, and communication capabilities in a passive system.
Table 2.1 Comparison between RFID systems, WSN, and RFID sensor networks[Atzori et al., 2010].
Processing Sensing Communication Range (m) Power Lifetime Size Standard
RFID No No Asymmetric 10 Harvested Indefinite Very
small
ISO18000
WSN Yes Yes Peer-to-peer 100 Battery <3 years Small IEEE
802.15.4
RSN Yes Yes Asymmetric 3 Harvested Indefinite Small None
2.6.3 Communication and Networking
Communication is done via a computer or data network. It’s a telecommunications network where nodes share data recourses with each other using a data link[Atis, 2017]. Computer networks differ in transmission medium, network sizes, communications protocols, and topology. The best known computer network is the Internet. Some examples of computer network types from small (NFC) to large (Internet) can be seen below:
• Near-Field Communication (NFC)
• Personal Area Network (PAN)
• Local Area Network (LAN)
• Wireless Local Area Network (WLAN)
• Cloud; Internet Area Network (IAN)
• Internet
For the IoT, all these networks need to be connected using the Internet. But the battle over the preferred networking protocol is far from over. There are multiple technologies like Bluetooth, Wi-Fi,
Low-Power Solutions, Zigbee, IEEE 802.15.4, 6LoWPAN, among others that can fulfill this role. For a local IoT network, or M2M-only, the discussed wireless protocols above are all good options. Since the goal is to transmit data over the Internet, IPv6 is the most logical choice. The connectivity requirements for IoT devices are so diverse that a single technology cannot meet all the range, power, size and cost requirements[micrium.com, 2015].
Miorandi et al. states that from a conceptual standpoint, the IoT builds on three pillars, related to the ability of smart objects to:
1. Be identifiable (anything identifies itself )
2. To communicate (anything communicates)
3. To interact (anything interacts)
Either among themselves, building networks of interconnected objects, or with end-users or other entities in the network. Despite the wide variety of technologies for the radio access, IPv6 at the transport layer enables the interconnection of all of them besides the possibility to address the expected billions of things that will be connected in the near future[Wu et al., 2010]. The IoT will be an immense network, which not only connects billions of things, but also encompasses heterogeneous networks[Karagiannis, 2014]. Since the IoT assumes the situation where "anything communicates", only bidirectional communication technologies are being used (PAN, LAN, WAN) [Lodewijks et al., 2016a]. This gives protocols that use IP packets an advantage compared to others. IPv6’s addressing scheme contains 128 bits and therefore has 1038possible addresses[Deering, 1998]. For efficient peer to peer communication IoT devices need to obtain a global IP address, which is much simpler with the amount of addresses generated by the IPv6 protocol. The usefulness of IoT devices resides not only in local communication, but also in global communication. If at all possible, it is crucial that your IoT networks (LANs, PANs, and BANs) all make use of the suite of Internet Pro-tocols (IP, UDP, TCP, SSL, HTTP, and so on)[micrium.com, 2015]. 6LowPAN uses an IPv6 address with a compressed header. This makes it easy to offers Internet connectivity, and with each device hav-ing a global address, it is simpler to obtain peer-to-peer communication[Shelby and Bormann, 2011].
This does not automatically mean that networks that do not use IP are useless. A non-IP network can be connected to the Internet using a gateway. A gateway is used to connect two different network types so that data can flow between them. This is usually used to obtain a connection between a local network and the Internet. Figure 2.8 gives a graphical representation of a gateway and the different assets it contains.
Figure 2.8 Gateway architecture[micrium.com, 2015]
2.6.4 Middleware
The IoT needs a software platform in order to perform the required functions like self-configuration, scalability and interoperability[Lodewijks et al., 2016a]. Middleware is defined by Atzori et al. as "a software layer or a set of sub-layers interposed between the technological and the application levels" and it will play a "major role in simplifying the development of new services and the integration of legacy technologies into new ones"[Atzori et al., 2010].
Middleware for the IoT often follows the Service Oriented Architecture (SOA) approach. The SOA approach decomposes complex systems into simpler and well defined components. A SOA approach also allows for software and hardware reusing, because it does not impose a specific technology for the service implementation[Pasley, 2005]. The SOA approach has many advantages that are recognized in most studies on IoT middleware solutions[Ibrahim, 2009] [Charles, 1999] [Masri and Mammeri, 2007]. Figure 2.9 shows the definition of the middleware sketch with its layered architecture. The scheme is based on the scheme of[Spiess et al., 2009] which also addresses the middleware issues of abstracting the devices functionalities and communications capabilities, providing a common set of services and an environment for service composition[Atzori et al., 2010]. For further reading about middleware, Farahzadi et al. did a survey about IoT middleware[Farahzadi et al., 2017].
2.6.5 Data Storage and Analytics
More and more devices will be connected to the Internet and start to deliver huge amounts of data. To manage all this data, the data should be more effective and efficient to reduce the amount of data which is not necessary. The challenge lies in reducing the amount of noise in generated data, by first filtering the data and then categorize it. To do so, policies concerning ownership, storage,
Figure 2.9 SOA-based architecture for the IoT middleware[Atzori et al., 2010].
and data expiry will become critical for smart monitoring, data storage and analytics[Gubbi et al., 2013]. Therefore it’s important to develop algorithms that make sense of the collected data. Artificial intelligence is a key aspect here since automated decision making is necessary to process the huge amounts of data collected. Also the cloud will play a big role in handling all the generated data, more about the cloud and big data can be read in section 2.8.
2.6.6 Applications
The emerging IoT will impact multiple application domains and the development of this domains. There are several application domains which will be impacted by the emerging IoT. The applications can be classified based on the type of network availability, coverage, scale, heterogeneity, repeatabil-ity, user involvement and impact[Gluhak et al., 2011]. These environments are now equipped with objects with only primitive intelligence, most of times without any communication capabilities [At-zori et al., 2010]. To make this objects communicate between each other and elaborate information imply a very wide range of applications. Both Atzori et al. and Gubbi et al. categorize the domains in four very similar categories[Atzori et al., 2010] [Gubbi et al., 2013]:
• Enterprise
• Utilities
• Personal and Home
There is a huge crossover in applications and the use of data between the domains. For instance, the Personal and Home IoT produces electricity usage data in the house and makes it available to the electricity company (utility) which can in turn optimize the supply and demand in the utility IoT[Gubbi et al., 2013]. The sharing of data between service providers is over the Internet, creating multiple business opportunities and a wide variety of applications where IoT will play a big part.
2.7
Internet Protocol Suite
Internet protocol suite is a communication protocol which consists of a set of rules for exchanging information between network links. In a protocol stack each layer leverages the services of the protocol below it. The Internet protocol suite uses a four layer model with the following attributes:
1. Application layer
2. Transport layer
3. Internet layer
4. Link layer
In Figure 2.10 the four layers that are at the heart of the Internet can be seen. The seven-layer reference model OSI is used to represent this. The three top layers are grouped together to simplify the model. Each layer has many options for different protocols, from which some are shown next to the corresponding layers.
The four layers are described by Forouzan in his book TCP/IP Protocol Suite [Forouzan, 2002]:
The Application layer
The application layer is the top layer in the Internet protocol, where applications create user data provided by the lower layers, and communicate this data between the different hosts. This layer contains the higher level protocols: HTTP, FTP, SSH, and SMTP.
The Transport layer
Below the application layer the transport layer can be found, which consists of the TCP and UDP protocols. TCP provides the concept of a logical connection, acknowledgement of transmitted
Figure 2.10 TCP/IP stack reference model [micrium.com, 2015]
packets, retransmission of lost packets, and flow control[Clark, 1988]. UDP is the basic transport layer protocol, providing an unreliable datagram service[Clark, 1988]. Both provide host-to-host communication between hosts.
The Internet layer
The Internet layer connects different networks and establishes the Internet, which is short for Inter-Network. This is where the ubiquitous IP addresses can be found and defines the routing structures for the Internet protocol suite.
The Link layer
The link layer describes methods used by a local network. The most common protocols are Ethernet, WiFi, GSM, 3G, LTE, 4G. This layer includes the protocols used to describe the local network topology and the interfaces needed to effect transmission of Internet layer datagrams to next-neighbor hosts [TCP/IP, 2017].
One of the most important protocol stacks today is HTTP (the World Wide Web protocol) run-ning over TCP over IP (the Internet protocols) over IEEE 802.11 (the Wi-Fi protocol)[Fall and Stevens, 2011]. This is the stack that is used between the wireless router and a computer or laptop surfing the web. It is commonly known as TCP/IP protocol because the original protocols in the suite are the
Transmission Control Protocol (TCP) and IP[steves-internet-guide.com, 2017]. The TCP/IP layers are more thoroughly described by Forouzan in his book TCP/IP Protocol Suite [Forouzan, 2002].
2.7.1 IoT Protocols
It is fundamental to investigate the characteristics of the traffic exchanged by smart objects as they should be the basis for the design of the network infrastructures and protocols[Atzori et al., 2010]. Multicast, low overhead, and simplicity are extremely important for the IoT and M2M devices, which tend to be deeply embedded and have much less memory and power supply than traditional Internet devices have[Colitti et al., 2011]. Therefore, efficiency is very important, and although it is possible to built an IoT system with familiar web technologies, newer protocols are developed that are more efficient. Web protocols normally use a significant amount of bytes that are send over the Internet. For IoT systems the main challenge is to reduce the amount of bytes that have to be send. Different protocols can be used for the IoT protocol like: HTTP, WebSocket, XMPP, CoAP, and MQTT. Table below contains a summary of the IoT protocol landscape according to Cisco, and their characteristics.
Table 2.2 A Cisco View on IoT Protocols[Duffy, 2017]
Protocol CoAP XMPP RESTful HTTP MQTT
Transport UDP TCP TCP TCP
Messaging Request/ Response Publish/ Subscribe
Request/ Response Request/ Response Publish/ SubscribeRequest/ Response 2G, 3G, 4G
Suitabil-ity (1000s nodes)
Excellent Excellent Excellent Excellent
LLN Suitability (1000s nodes)
Excellent Fair Fair Fair
Compute
Re-sources
10Ks RAM/Flash 10Ks RAM/Flash 10Ks RAM/Flash 10Ks RAM/Flash
Success Stories Utility Field Area Networks
Remote manage-ment of consumer white goods
Smart Energy Pro-file 2 (premise en-ergy management, home services)
Extending enter-prise messaging into IoT applica-tions
These Internet-specific IoT protocols have been developed to meet the requirements of devices with small amounts of memory, and networks with low bandwidth and high latency[micrium.com,
2015].
In the traditional Internet, the protocol utilized at the transport layer for reliable communications is the Transmission Control Protocol (TCP)[Cerf et al., 1974]. According to Atzori et al. it is obvious that TCP is inadequate for the IoT, due to the following reasons[Atzori et al., 2010]:
1. Connection setup: TCP is connection oriented and each session begins with a connection setup procedure. This is unnecessary, given that most of the communications within the IoT will involve the exchange of a small amount of data and, therefore, the setup phase would last for a considerable portion of the session time.
2. Congestion control: TCP is responsible of performing end-to-end congestion control. In the
IoT this may cause performance problems as most of the communications will exploit the wireless medium, which is known to be a challenging environment for TCP[Lakshman and Madhow, 1997].
3. Data buffering: TCP requires data to be stored in a memory buffer both at the source and at the destination. Management of such buffers may be too costly in terms of required energy for battery- less devices.
As a consequence, TCP cannot be used efficiently for the end-to-end transmission control in the IoT[Atzori et al., 2010].
Figure 2.11 compares the protocol stacks for web applications (left) against an IoT protocol (right). The protocol for web applications easily produces a data overhead of hundreds or thousands of bytes compared to IoT protocols which are optimized for constrained devices and networks, and produce a much smaller data overhead of tens of bytes[micrium.com, 2015].
The IoT protocol used in Figure 2.11 is just an example of a possible protocol for the IoT. It uses some of the promising techniques like 6LoWPAN, UDP and CoAP for its protocol, but many more protocols are possible. These will not be discussed in this paper, but the key aspects for future IoT protocols (especially in the maritime sector) is that they meet the requirements of devices with small amounts of memory, less power supply, and networks with low bandwidth and high latency [micrium.com, 2015] [Colitti et al., 2011].
Figure 2.11 Comparison of web and IoT protocol stacks[Shelby, 2013]
2.8
Big Data and Cloud Computing
Big Data (BD) and Cloud Computing (CC) are out of the scope of this paper, but since IoT, Big Data, and Cloud Computing complement each other and are almost always used together, a short explanation of the two will be given here. Interesting papers for further reading on big data and cloud computing are[Hashem et al., 2015] [Chen et al., 2015] [Gandomi and Haider, 2015].
Today an overwhelming amount of data is generated and analyzed by enterprises, social media, multimedia and the IoT: BD[Kambatla et al., 2014]. The data is numerous, it cannot be categorized within standard relation databases and the capturing- and processing processes are executed rapidly [Lodewijks et al., 2016b]. The underlying engine of Big Data is supported by Cloud Computing, which allows a much larger scale and more complex algorithms that can be employed to meet the, continuously growing, demands of Big Data[Lodewijks et al., 2016b]. However, the rapid evolution of Big Data left little time for the subject to mature in academic literature and there exists little consensus of the fundamental question when data is qualified as Big data[Lodewijks and Ottjes, 2005].
A popular definition of BD is that it’s high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization[Laney, 2001], [Gartner Inc., 2012]. The above definition is coherent with
the 3Vs model for describing BD. IBM introduced a fourth ’V’ - veracity, concerning about the quality of data, because biased data can only produce wrong answer. Very recently, it has been argued that a fifth ’V’ - value might be even more important than the previous four V’s, because it is only sensible for a business to adopt big data strategy if it could generate value considering the costs[Wang et al., 2016]. The growth of cloud computing and big data further promote the growth of the IoT [Chen et al., 2015]. The steps that are used in order to extract information from big data fall under the big data value chain[Lodewijks et al., 2016b], Figure 2.12.
CHAPTER
3
MARITIME TRANSPORT
This chapter will explain what Maritime Transport (MT) is, which parties are involved, and which components MT has. At the end of the chapter the current technologies for communication are explained.
Figures 3.1 shows the exponential growth of the global freight transport over the last decades, and forecasts are that this growth will continue for the next years. According to a new market report published by Persistence Market Research, the global market for shipping containers is estimated to reach value of 8.29 Bn USD by 2015 end, and is expected to expand with an annual rate of 5.6% from 2015 to 2021, to reach a market value of 11.47 Bn USD by 2021[Persistence Market Research, 2015].
3.1
Definition of Maritime Transport
MT has many definitions that can be found in literature. The International Maritime Organization (IMO) is an organization that is responsible for shipping. IMO is the United Nations specialized agency with responsibility for the safety and security of shipping and the prevention of marine pollution by ships[IMO, 2017]. IMO states that maritime transport is the carriage of goods and passengers in sea-going vessels including all the governments, organizations and stakeholders involved with the day-to-day business of the shipping industry[IMO, 2017].
Figure 3.1 Global freight transport in trillions of tonne-kilometers[OECD, 2017].
According to[Mullai and Paulsson, 2011] MT is:
"A very complex and large-scale socio-technical environment system comprising human and man-made entities that interact with each other and operate in a physical environment".
According to[Veenstra, 2002] the definition of MT is:
"The basic concept that maritime transport relates to the carriage of goods or/and passengers by sea by a person for commercial purposes, either in return for payment (i.e. for hire and reward) or on an organization’s own account as part of its wider economic activity. Here goods transportation refers to the volume of containerized, dry bulk, liquid bulk and roll on-roll off (Ro-Ro) type of cargo handled by the ports while passenger traffic refers to the number of national, international and cruise passenger volumes transported through ports."
In general MT consists of the entire value chain related to maritime transport, including the logistics before and after the shipping phase. In this paper only the shipping phase will be analyzed, and only briefly how it connects with the other systems before and after this phase. Furthermore, IMO states that 90% of the global shipping is cargo related[IMO, 2017]. Therefore the focus in this paper will lie on cargo vessels, resulting in the following definition of MT:
MT is a system where maritime transport relates to the carriage of goods using cargo vessels.
3.2
Which Parties and Actors are involved?
According to[Pietrzykowski, 2011], MT can be divided in the following three sub-systems: 1. Elements
2. Processes
3. Tasks
Pietrzykowski describes this three sub-systems as follows[Pietrzykowski, 2011]:
1. "Transport system elements are objects taking part in the process of moving cargoes and objects related with the movement process. These include: seaports, sea-going ships as cargo carrying vehicles, waterways, equipment and traffic arrangements including regulations for traffic safety and control.
2. The transport system approached dynamically corresponds to the dynamic transport process. This process can be divided into sub-processes taking place within the system and those taking place outside and forcing action in the transport system. The sub-processes inside the system, in turn, can be divided into decision and technological sub-processes. These processes are determined by technical, economic and organizational constraints as well as those imposed by the environment.
3. The system tasks result from the major function of transport services: carriage of cargo. These tasks include organization, control and supervision of the transport process, which in turn, consists of such operations as loading/ embarkation, carriage by ship, unloading / disem-barkation. From another perspective these operations are services offered by the system and rendered by its sub-systems. These include, but are not limited to:
• Freight and fleet management • Vessel traffic management
• Safety management and damage control management
• Management of information for shipowners, marine agents, vessel commanders travel-ers, administrative bodies and others."
Kristiansen lists the system and functions of a ship in tables 3.1 and 3.2. These systems and functions of a ship are important to analyze the ship, and where IoT can help to improve these. This will be further explained in chapter 4.
Table 3.1 Systems of a ship[Kristiansen, 2005].
Systems
Accommodation and hotel service Communications
Control Electrical Ballast Lifting
Machinery and propulsion Management support systems Positioning, thrusters
Radar
Piping and pumping Pressure plant, hydraulics Safety
Table 3.2 Functions of a ship[Kristiansen, 2005].
Functions
Anchoring
Carriage of payload Communications
Emergency response and control Habitable environment
Maneuverability Mooring
Navigation
Pollution prevention Power and propulsion Bunkering as storing Stability
Structure
With all the systems and functions of the ship known, the actors can be determined. Multiple different papers Pietrzykowski[Pietrzykowski, 2011] [Trucco et al., 2008] [IMO, 2017] [Lag, 2015] describe the major participants in MT:
• Shipowners to optimize transport service operations of their fleets
• Companies responsible for the organization and execution of unloading/loading processes. • Operators responsible for the organization and supervision of vessel traffic within port
ap-proaches and basins; to efficiently handle ingoing and outgoing traffic: assist in collision avoidance, control movements of ships, minimize their waiting times before arrival and departure.
• Safety and security services; safety and damage control management - organization and command of rescue operations.
• Navigators on watch to complete a sea voyage according to shipowner’s instructions and, at the same time, observe the rules of safe navigation, regulations in force and instructions of operators responsible for the organization and supervision of vessel traffic.
3.3
Opportunities
The detection, tracking, trajectory estimation and trajectory prediction of maneuvering vessels are important facilities for navigation systems as well as the Vessel Traffic Monitoring & Reporting (VTMR) systems to improve safety, security and survivability in ocean navigation[Perera and Soares, 2010]. Advancements in Information Communication Technologies (ICT) create opportunities for improving performance of transport in the maritime sector.
These current technologies offer not enough interaction between the systems, functions, and actors mentioned in section 3.2. To improve the quality and efficiency of the maritime transport link in the global chain, advancement of information exchange is needed between the different actors. Implementing IoT in MT can make these opportunities a reality. This is already the case in other transport systems in aviation and onshore, but in the maritime sector this is still lagging behind. The concepts of Motorways of the Sea (MOS), e-Navigation and e-Maritime show that there is a need for setting forth guidelines for and architecture of maritime intelligent transport systems[Pietrzykowski, 2011]. These concepts, how IoT will help improving MT and the challenges are explained in chapter 4.
CHAPTER
4
IOT IN MT
This section will describe IoT in MT as described in chapters 2 and 3. First the possibilities of IoT in MT are described, and how IoT can improve the Maritime Sector (MS). Ship connectivity is one of the most important things when it comes to enabling IoT and this topic is discussed in section 4.2. This and the main challenges will be discussed in the last section of this chapter.
4.1
Possibilities for IoT in MT
There are some estimations about the global trade by the maritime sector, reaching from around 80% of global trade by volume (70% of global trade by value)[United Nations Publications, 2015] [Ericsson, 2015b], to estimations of 90% of the global trade market [IMO, 2017] [International Chamber of Shipping, 2017]. This means that ships carried an estimated of 9.6 billion tons of cargo in 2013, but the maritime industry lags behind compared to alternative transport industries in terms of its use of information and communication technologies[Ericsson, 2015b]. In a system that big and complex, efficiency becomes the name of the game. Yet, crucial maritime transport data, from vessel location and cargo IDs to maintenance data and port availability is still being sent point to point rather than made available to all parties simultaneously via a network[Ericsson, 2017]. This is a time consuming process and the lack of access to real-time data significantly increases the margin for error. By taking
the IoT from land out to sea, the entire maritime system would benefit[Ericsson, 2017].
By enabling real time data and information to the different stakeholders, the entire supply chain can be streamlined. Trucks will spend less idle time at port, cargo will spend less time in transit, and producers can better plan their next shipment[Ericsson, 2017]. By bringing real time data end to end to the maritime transport industry, the industry will see more automation and intelligence in the future. IoT solutions can have a significant impact on maritime operations such as route optimization, asset tracking and equipment monitoring[Tracy, 2017].
4.1.1 Route Optimization
The detection, tracking, trajectory estimation and trajectory prediction of maneuvering vessels are important facilities for navigation systems as well as the VTMR systems to improve safety, security and survivability in ocean navigation[Perera and Soares, 2010]. Ships have been using high frequency radios to communicate with other vessels for years to determine the most efficient routes and avoiding collisions. Unfortunately, this once-revolutionary technology lends itself to the potential for user error[Fagerhus, 2015]. Lag describes some of the technologies that are currently used for navigation in his paper[Lag, 2015]:
• Two way Voice Communication (by Radio or Satellite)
• Automatic Identification System (AIS)
• Long-range Identification and Tracking (LRIT)
• Vessel Traffic Service (VTS)
The above applications are mandated by regulations, and do not require broadband data con-nections, but to further improve efficiency, real time data should be made available to all parties. One of the key concerns of shipowners is keeping fuel costs down, as these are a major part of the operational cost picture, and also a major source of emissions that is harmful to the environment [Schultz et al., 2011] [Lag, 2015] [Ericsson, 2015a]. Route optimization can have a big impact on reducing the fuel consumption and keep emission levels to a minimum. Using real time data from the IoT by connecting all parties to the Internet can have a great impact on route optimization. Route optimization is a complicated task for ships since there are many factors that can influence this. Next to the factors that are not likely to change during shipping, e.g.: port charges and efficiency, loading/unloading charges, location of feeder ports, etc. [Hsu and Hsieh, 2007], there are also factors that can change during shipping, e.g. weather, traffic, or accidents. All these factors are important issues when determining the optimal route, ship size, and sailing frequency, and directly influence
