Horizon 2020

Project title: Software-defined Intermittent Networking

Reference Scenario and Requirements

Definition

Deliverable number: D1.1

Version 1.0/1.0

Funded by the European Union’s Horizon 2020 research and innovation programme

under the Marie Sklodowska-Curie Grant Agreement No. 699924

Project Acronym: SINet

Project Full Title: Software-defined Intermittent Networking

Call: H2020-MSCA-IF-2015

Grant Number: 699924

Project URL: http://sinet.dpalma.eu

Editor: David Palma, ITEM-NTNU

Deliverable nature: Report (R)

Dissemination level: Public (PU)

Contractual Delivery Date: M02

Actual Delivery Date M02 (publicly released in M06 with D1.2)

Number of Pages: 22

Keywords: Reference Scenario, Maritime Operations, Networking, Communications, Requirements,

Unmanned Vehicles

Authors: David Palma ITEM, NTNU (main author)

Artur Zolich IKT, NTNU

Roger Birkeland IET, NTNU

Peer review: Yuming Jiang ITEM, NTNU

Tor-Arne Johansen IKT, NTNU

Abstract

The increasing interest in oceanographic environmental research has led to the use of coordinated remote-sensing systems

in maritime environments. This interest has been supported by the current growth and innovation in unmanned vehicles

and communication technologies, which create several new opportunities for researchers. State-of-the-art scenarios and

technologies for maritime environments are reviewed, reinforcing the potential of integrating ICT to enhance environmen-

tal research-data gathering. A comprehensive analysis of emerging robotic, communication and networking technologies

is presented and discussed in the context of maritime environments. The existing capabilities, limitations and availability

result in the conceptualisation of an infrastructure for improving access to research data, achieved through an Internet-

compatible integration layer for heterogeneous unmanned vehicles. A reference scenario and requirements are presented,

enabling a future generation of coordinated remote-sensing maritime systems. This aligned with the need for retrieving

improved research-data and allows for safer and more efficient operations in the challenging conditions offered by oceans

and seas.

Contents

1 Introduction 1

2 Background in Heterogeneous Maritime Environments 3

2.1 Information and Communication Technologies (ICT) and Robotics in Maritime Scenarios . . . . . . . . . 3

2.1.1 Heterogeneous & robotic operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4

2.1.2 Moored and quasi-static operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5

2.2 Technologies and Opportunities in Maritime Environments . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2.1 Robotic Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7

2.2.2 Communication Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9

2.2.3 Networking Technologies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10

3 Internet of Maritime Things 113.1 Reference Scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11

3.1.1 Maritime Remote-Sensing Areas . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.1.2 Coordinated Unmanned Vehicles . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

3.2 Challenges and Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.2.1 Non-functional Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.2.2 Functional Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14

3.3 Software-defined Intermittent Networking and Communications . . . . . . . . . . . . . . . . . . . . . . 14

4 Conclusions 18

List of Figures

1.1 Coordinated remote-sensing systems in maritime environments . . . . . . . . . . . . . . . . . . . . . . . 1

2.1 Remote-sensing systems in maritime environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3

2.2 Examples of unmanned vehicles currently used in maritime scenarios for remote sensing . . . . . . . . . 7

3.1 Co-existence of heterogeneous communications and vehicles . . . . . . . . . . . . . . . . . . . . . . . . 11

3.2 Remote-vehicle type per scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13

3.3 Classes of communication links . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

List of Tables

2.1 State-of-the-Art Maritime Systems using ICT and Robotics . . . . . . . . . . . . . . . . . . . . . . . . . 3

3.1 Summary of Configurations for Coordinated Remote-Sensing Systems in Maritime Environments . . . . 17

List of Acronyms

6LoWPAN IPv6 over Low power Wireless Personal Area Networks

ADCP Acoustic Doppler Current Profiler

AIS Automatic Identification System

AUV Autonomous Underwater Vehicles

C&C Command and Control

CTD Conductivity Temperature and Depth

DODAG Destination-Oriented DAG

DTN Disruptive or Delay Tolerant Network

DSC Digital Selective Calling

GMDSS Global Maritime Distress and Safety System

GNSS Global Navigation Satellite System

GW Gateway

HALE High Altitude Long Endurance

HD High-Definition

HF High Frequency

ICT Information and Communication Technologies

IETF Internet Engineering Task Force

IP Internet Protocol

IPv6 Internet Protocol version 6

IMC Inter-Module Communication

IoT Internet of Things

ISM Industrial, Scientific and Medical

LAUV Light AUV

LOS Line-of-Sight

MALE Medium Altitude Long Endurance

MANET Mobile Ad-hoc Network

MTOW Maximum Take-Off Weight

PAN Personal Area Network

RA Route Advertisement

RPL IPv6 Routing Protocol for Low-Power and Lossy Networks

SA Situational Awareness

SDN Software-Defined Networking

SNR Signal to Noise Ratio

UAV Unmanned Aerial Vehicle

USV Unmanned Surface Vehicle

UV Unmanned Vehicle

VHF Very High Frequency

WG Working Group

WSN Wireless Sensor Network

1 Introduction

Oceans cover more than 70% of the Earth’s surface, being closely tied with weather and climate changes. In addition to

the direct impact on the Earth’s biosphere [1], the oceans are crucial for transportation between countries and continents,

among other activities and sectors of economic value such as fishing, mining or tourism [2].

Operating in oceans and seas across the world are various types of vehicles, manned and unmanned, as well as different

ICT and infrastructures such as oil platforms, fish farms, buoys and sensors. Despite the introduction of new ICTs and

robotic systems such as Unmanned Aerial Vehicles (UAVs), several challenges still exist in satisfying the increasing need

for better and continuous access to environmental data.

This deliverable provides an analysis of existing challenges and opportunities for maritime remote-sensing in a two-fold

contribution:

1. Presentation of state-of-the-art scenarios and technologies for remote sensing in maritime environments;

2. Conceptualisation of a reference scenario and requirements for challenging maritime operations.

Figure 1.1: Coordinated remote-sensing systems in maritime environments1

The monitoring of new transportation routes, such as the Europe to Asia link through the Arctic, is expected to become

more widely used [3]. This leads to the need of new systems capable of supporting ships in their navigation in extremely

challenging conditions, complementing existing sensors increasing precision and accuracy [4]. Such a system may com-

prise UAVs or other vehicles, used to travel in front of the ships and perform scheduled scouting surveys, preventing

threats such as piracy, floating and drifting debris/icebergs, among other things.

The growth in use of not only UAVs but also other unmanned vehicles (e.g. underwater and gliding units), is becoming

prevalent in remote-sensing due to their advantages, illustrated by Figure 1.1. These systems have already been used in

different research-fields in order to achieve higher-levels of precision and accuracy [5, 4], where the use of a real-time

link may be required as input for conducting the research process [6]. Additionally, the cooperation and integration of

UAVs with other sensors and vehicles, in a multi-sensor coordinated-observation approach (e.g. sensors already deployed

or carried by other vehicles), can also be used to improve the data-acquisition process and overall performance [7, 6, 8, 9].

1SeaExplorer Glider image licensed under the Creative Commons Attribution-Share Alike 3.0 Unported (https://creativecommons.org/licenses/by-sa/3.0/deed.en)

Deliverable D1.1 1

1. Introduction

In particular, the integration and coordination of these vehicles and systems have already been envisaged in a near future,

aiming at heterogeneous remote-sensing systems for maritime environments [10, 11, 12, 13].

The impact of ICTs and robotics in oceanography, and other maritime affairs, is also reflected in the increasing demand

for higher bitrates (i.e. data transfer rates), real-time communications and robustness. Nonetheless, the monitoring of

these systems and the acquisition of research-data are challenged by data communications. In maritime scenarios, the

lack of infrastructures considerably limits the access to telecommunication technologies as data-links (i.e. to retrieve

research-data), and nowadays the use of satellite systems, or other long-range and low-bitrate communication systems, is

the prevalent solution.

Currently, voice or very low data-rate communications are the existing alternatives for maritime-based communication

systems. For example, the Global Maritime Distress and Safety System (GMDSS) uses Digital Selective Calling (DSC)

to send pre-programmed digital messages through standard maritime radios such as Very High Frequency (VHF). A

different technique is employed by the Automatic Identification System (AIS), which introduces information about po-

sition, speed and heading, increasing Situational Awareness (SA) and, though limited to this information, preventing

collisions. Regarding scientific data, very few communication options are available. For instance, satellite links such as

InmarsatTM and IridiumTM can allow global connectivity but have the drawback of being costly (financially and in energy

efficiency), and provide only limited bandwidth.

Due to the cost and limitations of satellite links, in particular for research purposes where a single sensor may generate

megabytes of data per minute (e.g. sonar data [14]), scientific missions often require manned missions to deploy and

retrieve scientific equipment after a given period of time, even when using unmanned vehicles such as Autonomous

Underwater Vehicless (AUVs) [15], which typically rely on low-bitrate acoustic links. This lack of appropriate and widely

available data-links restrains scientists in what can be done. For example, adapting the data-acquisition process according

to received samples or environmental condition may be desirable, as well as combining sensors and actuators [16], which

is mostly disregarded today. Moreover, the planning of manned missions for deploying and collecting sensors is extremely

complex and costly, raising considerable crew-safety concerns, in particular when missions are far from shore.

Despite the lack of available communication infrastructures, the coordination of heterogeneous systems introduces the

possibility of creating a communication network between different vehicles. By handling these different systems appro-

priately, cooperation can be promoted between different data producers and consumers without compromising critical

operations, benefiting all the intervening actors. Nonetheless, this reality is yet to be fully exploited, and the used tech-

nologies depend mostly on what maritime actors acquire for their specific needs and vehicles, disregarding an interoper-

able networking opportunity. In fact, off-the-shelf equipment for communication purposes is typically employed in these

scenarios [17], and there is a need for common standards and interfaces to interconnect such systems.

This deliverable highlighting available possibilities and technologies capable of enabling next-generation networking and

communication systems for challenging maritime environments, including also the use of unmanned vehicles, standards

and protocols suitable for a network of heterogeneous sensing platforms. For this purpose, directions regarding preferred

unmanned vehicles, communication and networking solutions are presented, taking into account different scales and

requirements that may be associated with scientific missions.

The overview of the two state-of-the-art remote-sensing scenarios is presented in Chapter 2, using Unmanned Vehicles

(UVs) coordinated with other vehicles and infrastructures in real maritime operations. Chapter 3 presents a reference

scenario, requirements and future directions, followed by a conclusion Chapter 4.

Deliverable D1.1 2

2 Background in Heterogeneous Maritime Environments

ICT and Robotics have been used in Maritime Environmental Operations from self-contained fish monitoring research

scenarios, to complex and experimental operations such as the navigation of large-scale unmanned vehicles aided by

smaller-scale unmanned vehicles for surveying. This approach is becoming increasingly more popular as technologies

develop and reveal their potential in challenging or extreme environments.

2.1 ICT and Robotics in Maritime Scenarios

(a) Robotic Systems1 (b) Moored Operations

Figure 2.1: Remote-sensing systems in maritime environments

The following subsections present two different state-of-the-art scenarios, from which the pictures in Figure 2.1 were

taken. Both scenarios used Unmanned Aerial Vehicles simultaneously with other manned and unmanned systems. These

scenarios also took into account the networking and communication needs for the required data-links, ensuring that

collected sensor-data was transmitted to researchers and control centres without the need for manual data collection.

Table 2.1 highlights the systems used in the presented scenarios, providing an overview of the main technologies and

requirements associated with each one. In particular, it reinforces the need for an integrated and coordinated observation

system in future oceanography endeavours, taking advantage of common vehicles and technologies for the different

scenarios using, for instance, standard networking protocols.

Table 2.1: State-of-the-Art Maritime Systems using ICT and Robotics

1Courtesy: LSTS, FEUP, Univ. of Porto

Deliverable D1.1 3

2. Background in Heterogeneous Maritime Environments 2.1. ICT and Robotics in Maritime Scenarios

2.1.1 Heterogeneous & robotic operations

Heterogeneous robotic operations in maritime environment are being demonstrated more and more often, typically re-

sulting from a joint effort between multiple institutions and interdisciplinary fields of research. An example of such an

operation was the Sunfish Tracking experiment2 in which UAVs and AUVs, depicted in Figure 2.1a, as well as Unmanned

Surface Vehicles (USVs) and manned vessel operations, were combined to track Mola-mola fishes [8, 18]. These require

continuous monitoring from several sensors, from cameras to sonars, in order to allow biologists to better understand their

behaviour and environment.

The Mola-mola fishes are known for staying at the surface, from time to time, in order to warm up through the sun’s

radiation. This behaviour allowed the tagging of fishes with a custom-made device, equipped with a Global Navigation

Satellite System (GNSS) receiver and a satellite transmitter, in order to track them while at the surface. Even though

the used device provided information about the fishes’ position, their next resurfacing is unknown, and therefore the

experiment has significant dynamics when trying to gather all the required data. This is one of the reasons why several

types of vehicles, monitored and coordinated from an onshore centre, were used during the operation. High- and low-level

control over the vehicles and the entire system was conducted using the LSTS Toolchain [19]. The unmanned vehicles

were used to gather a wide set of data about the environment where Mola-mola fishes live.

The issues and chosen solutions regarding the use of unmanned vehicles, communication technologies and networking

options, are presented in sections 2.1.1.1, 2.1.1.2 and 2.1.1.3 respectively.

2.1.1.1 Unmanned Operations

UAVs, AUVs, and USVs are characterised by very different capabilities, spatial domain, time of operation and deploy-

ment, and sensors. In the Mola-mola monitoring experiment, whenever a tagged fish re-surfaced and the tag connected to

the GNSS and acquired satellite signal coverage, its position was sent to an Internet server. This allowed the researchers,

in the control centre, to trigger the appropriate actions. For example, the USV constantly operating in the region was

commanded to navigate to the position of a fish at the surface, while registering required water parameters. Additionally,

a manned vessel with a team of researchers followed, with the intention of deploying an AUV nearby to scan water col-

umn parameters. Finally, if the fish’s position was within the range of the available UAV, another team of researchers was

prepared to launch the aeroplane to track and capture video footage of the surfacing animal.

The UAV used in the experiment was based on a SkywalkerTM X8 platform, equipped with a High-Definition (HD) camera

and capable of a flight time of 60 minutes. This unit has a range of 8 to 10 km, with a cruise speed of 18 m/s.

The AUV used in the experiment was a Light AUV (LAUV), capable of operating under the water for up to 8 hours,

reaching a speed of 3 kn, and a depth of 100 meters. It was also equipped with a Conductivity Temperature and Depth

(CTD) sensor, a fluorometer and an HD camera. Additionally, side-scan sonars and multibeam echo sounders could also

be mounted if required by the researchers for other measurements.

The USV was a WaveGliderTM, which is a boat powered by waves and capable of moving with speeds between 0.5 and

1.6 kn, depending on the sea conditions. It was equipped with an Acoustic Doppler Current Profiler (ADCP), a CTD and a

weather station. Passive propulsion and solar energy harvesting technology allows this vehicle to perform very extensive

missions in time (years), limited only by maintenance needs.

2.1.1.2 Communication Systems

Due to the usage of different sensors for acquiring distinct types of data, the system has also significantly variable data-rate

requirements for each component. Moreover, the heterogeneous nature of each device (i.e. aerial, underwater, surface),

also required different communication technologies to be used during this experiment.

The fish tag position, consisting of very few bytes, was reported using the SPOTTMSatellite system 3, which provides an

Internet-based backend to transmit data. SPOTTM could also be replaced by other satellite systems such as ARGOSTM 4

and IridiumTM 5, which provide similar functionalities as data links but with different costs and availability/precision. For

example, the used USV node had also low data-rate requirements and relied on IridiumTM links.

In this experiment, the sensors and control system used in the UAV required a bitrate in the order of tens of megabits

per second, for which an IEEE 802.11 link was used. This link was also used by the AUV for transferring research data

2http://sunfish.lsts.pt/en3http://www.intelligence-airbusds.com/en/191-spot-technical-information4http://www.argos-system.org5https://www.iridium.com/

Deliverable D1.1 4

2. Background in Heterogeneous Maritime Environments 2.1. ICT and Robotics in Maritime Scenarios

while on surface, which required a data-rate similar to the UAV. Additionally, underwater acoustic modems were used

to transmit control data, not for research data, which is not the focus of this analysis on existing remote-sensing systems.

The preferred backup link for transmitting information, whenever IEEE 802.11 was not in range, relied on IridiumTM so

that the teams could move closer to the AUV.

Regarding the communication between the research teams in manned vessels, satellite Broadband Internet links (using

Very-Small-Aperture Terminals, VSAT) and Cellular Network modems were used to exchange data between them. This

allowed these vehicles to act as gateways to the remaining devices, using their IEEE 802.11 links whenever available.

2.1.1.3 Networking

This experiment used the networking capabilities of the LSTS Toolchain, which relies on the custom Inter-Module Com-

munication (IMC) protocol for interconnecting different systems. The provided architecture allowed the received mes-

sages to be eventually aggregated by the toolchain’s Hub [19], if desirable, which then allows data to be accessed through

the Internet using standard protocols such as TCP/IP. Even though IMC is not a standardised protocol, it is open-source

and therefore can be used by other research initiatives. All the acquired information can be seamlessly forwarded through

devices using the LSTS Toolchain and directly visualised on their Neptus C4I (Command, Control, Communications and

Intelligence) software, also open-source. The system’s configuration is highly dependant on this toolchain, which requires

a pre-configuration of the entire system. On the other hand, this system allows the use of heterogeneous technologies,

such as Satellite and Acoustic links, which lack a common network layer or interface.

2.1.2 Moored and quasi-static operations

There is a significant number of moored or drifting nodes deployed all over the world. The Global Ocean Network

statistics list 125518 moored systems active between 2002 and 2016 [20]. For instance, buoys can be moored to fish

migration routes, collecting information on previously tagged fishes, which allows researchers to better understand their

behaviour [21].

Progress in Wireless Sensor Networks (WSNs) creates new opportunities for data collection in moored and quasi-static

systems. These systems can produce an extensive amount of information every day, making frequent data-collection a

desirable feature. For the scenario that deals with the monitoring of tagged fishes, low-power and low-range devices

were used in order to increase the overall lifetime of the system. In this case data was remotely collected using UAVs

for extending communication links, depicted in Figure 2.1b. However, concerning the use of unmanned vehicles as data-

mules or relay-nodes, USVs have also been proven as suitable options, where a WaveGliderTM was used to collect data

from 184 underwater tracking systems, fixed over 205 km of distance [22].

Another type of moored operation, being conducted by the ArcticABC project, involves extensive data collection from

several different and complex sensors, including Underwater Hyperspectral ImagerTM (UHITM) units, HD cameras and

Acoustic Zooplankton Fish ProfilerTM units (AZFPTM) [23]. Such systems can also be deployed in locations where

no suitable satellite coverage, or other typical communication systems, are available and where physical access is very

limited. In order to circumvent these limitations and high-bitrate requirements, UAVs are being considered for data

collection, in which moored nodes will gather information about the Arctic environment [24].

Moored or quasi-static systems with similar characteristics typically require data to be manually collected, often a few

times a year, due to the complexity of the research equipment that may generate gigabytes of data every day. Such

operations require a significant crew involvement, often including costly vessel time and scuba-divers. Manually picking

up nodes/data does not scale either, as there are many harsh scenarios where environmental hazards do not encourage or

permit human presence. To make things worse, in several situations there is no near-real-time access to the nodes’ data

and status, and therefore researchers have no insight into the situation and cannot react to changes or failures. Bearing

this in mind, near-real-time access to data would allow researchers not only to access relevant environmental information

faster, but also to reduce the operational costs and the risk of losing data [25].

The following sections, 2.1.2.1, 2.1.2.2 and 2.1.2.3, describe the issues and chosen solutions for the presented moored

operations and used unmanned vehicles, communication technologies and networking options.

2.1.2.1 Unmanned Operations

Buoys can be seen as unmanned nodes capable of carrying several different sensors, enabling them with a physical

infrastructure and communication capabilities. They are characterised by their sensors and lifetime expectation, which

Deliverable D1.1 5

2. Background in Heterogeneous Maritime Environments 2.1. ICT and Robotics in Maritime Scenarios

commonly spans for at least a year. This requires wise and efficient energy management, such as task planning and

communication scheduling. The communication technologies may rely on satellite links, but typically require additional

interactions to collect the vast amount of data generated by different nodes. These interactions can result from manned

expeditions to manually retrieve the data, or from the use of additional unmanned vehicles such as UAVs, USVs or AUVs.

In the described scenarios, UAVs were used as data-forwarding nodes instead of being used to carry specialised sensors.

Their modus operandi should take into account the vehicles’ endurance and speed in order to be able to approach the

sensing sites, which should be located between a few hundred metres to few kilometres of distance from the UAVs’ next

contact point. After reaching the destination, the UAVs loiter in the area in order to either retrieve or forward the available

data.

As previously mentioned, energy efficiency in these systems is of paramount importance. As such, the departure of UAVs

from supporting vessels or other infrastructures such as docking stations [26], should take into account the scheduling

of radio activation from the buoys side. By doing so, the available communication window between unmanned nodes

is optimised and more data can be gathered. This type of approach was used in the scenario for collecting information

about fish tags [21], depicted by Figure 2.1b. In that experiment, the buoys’ radios were activated at a predefined moment

and, simultaneously, a quadrocopter UAV would be hovering between the nodes and the user station, after taking off

from a support barge. Using WSN radios (i.e. IEEE 802.15.4 based), the UAV was used as a relay node and the full

communication path between the nodes and the user was created, enabling remote data retrieval.

As an alternative to UAVs, USVs have also an important role in what concerns data retrieval in remote locations. In

particular, solar-powered USVs can be used as data mules capable of travelling across large areas of the ocean. This

approach has an inherent delay, as nodes have limited speeds and also depend on environmental conditions but, on the

other hand, the use of USVs has a lower operation cost, and longer endurance, and typically does not require special

permissions or certificates.

AUVs have also been explored as data mules in similar scenarios, exploiting the use of short-range optical links for

increased data transfers, as opposed to the typically used acoustic links [27]. This approach may also be advantageous

assuming that AUV docking stations are deployed, creating the appropriate conditions for operating underwater [28],

similarly to UAV docking stations.

2.1.2.2 Communication Systems

The scenarios typically explored in moored operations, often consider the use of WSN technologies in situations where

low-power, low-bitrate and short distance (few hundred meters) communication is necessary. Examples can be air-to-

water communication using IEEE 802.15.4 networks or other low-power radios [29, 30]. However, due to the possibly

large amount of generated sensor data, higher data-rate radios may be desirable.

In such scenarios surface nodes have limited power, which favours WSN technologies over satellite communication, and

low antenna elevation, being typically placed in an array of a few hundred meters from each other. These nodes can

collect various types of data from simple water parameters to high resolution images, which require different types of

communication technologies. To increase the communication range of such nodes, as well as their data rate, unmanned

vehicles can be used to provide additional communication capabilities. For instance, by using UAVs as elevated relay-

node antennas, or by approaching the nodes close enough with USVs, unmanned vehicles are a suitable tool for data

collection or for mediating the data-link between the node and the user.

Deliverable D1.1 6

2. Background in Heterogeneous Maritime Environments2.2. Technologies and Opportunities in Maritime Environments

2.1.2.3 Networking

Even though unmanned vehicles create new communication opportunities and increased bitrate, the large amount of

generated data may still exceed the available transmission capacity of a network. For this reason, data prioritisation

should be handled, giving right of way to critical or delay-sensitive data.

The high number of available nodes already deployed around the world should also promote cooperation between them,

creating a network for different manned or unmanned operations. However, this is still not a reality, mostly due to not

applying standard protocols. From the mission planning point-of-view, the automatic configuration of network-capable

nodes is a desirable feature, where address auto-configuration features from the Internet Protocol version 6 (IPv6) or the

IPv6 over Low power Wireless Personal Area Networks (6LoWPAN), as well as other low-overhead routing protocols,

could be exploited (c.f. Section 2.2). This would enable visiting vehicles or new nodes to seamlessly integrate existing

networks and improve the its coverage and capabilities. In addition to allowing the deployment of new nodes in a “plug

and net” fashion, such an automatic configuration of the network would also be important to adapt the network topology

(i.e. routing paths), as the network topology will likely change with time – Line-of-Sight (LOS) can be obstructed by

vessels, or sea state – maintaining a dynamic mesh or hierarchical network.

2.2 Technologies and Opportunities in Maritime Environments

The previously described scenarios introduced several concepts and approaches that integrate the use of Unmanned Aerial

Vehicles together with other unmanned vehicles and ICT to perform different remote environmental sensing operations.

These scenarios use different robotic technologies (illustrated by Figure 2.2), communication systems and even different

networking approaches. The following sub-sections address each of these topics, presenting existing solutions and their

potential for being used in future maritime environmental research activities.

2.2.1 Robotic Technologies

A wide range of unmanned vehicles are currently available in the marine domain, used in conjunction with manned vehi-

cles as well as more persistent infrastructures such as satellites in space, seabed transponders and onshore communication

assets. This section presents an analysis of existing maritime unmanned vehicles, their characteristics and possible uses

in remote sensing.

(a) UAV

(b) USV (c) AUV (d) Buoy

Figure 2.2: Examples of unmanned vehicles currently used in maritime scenarios for remote sensing

Deliverable D1.1 7

2. Background in Heterogeneous Maritime Environments2.2. Technologies and Opportunities in Maritime Environments

2.2.1.1 Unmanned Aerial Vehicles (UAVs)

There are a few thousand different UAV platforms available [31]. Many of them are developed with focus on specific

tasks and provide various capabilities. In general, UAVs can be categorised by their size, endurance and take-off/landing

requirements, which this work considers by following guidelines from the European Aviation Safety Agency [32].

One of the most popular groups of UAVs are multirotor platforms, capable of vertical take-off and landing. They often

provide limited payload capabilities and endurance, but can operate from small areas and hover, which can be very

convenient in some situations. Tethered systems (e.g. blimps) can also be considered as small UAVs [32], but are limited

in what they can do, being mostly used as part of the communication infrastructure (e.g. as elevated antennas/radio

nodes) [9].

For longer endurance and faster travel, fixed-wing aircraft are used. They are available in a wide variety of sizes and

capabilities. The smallest vehicles, considering their feasibility in maritime scenarios, have a Maximum Take-Off Weight

(MTOW) of few kilogrammes, which is enough for carrying basic scientific/communication equipment and stay airborne

for about one to two hours. Bigger platforms, around 25 kg MTOW, can provide much longer flight times – from few hours

up to a day – and more advanced payload options, still limited to few kilogrammes of the equipment. The next group of

vehicles, below 150 kg MTOW, can carry heavier payloads and provide long-flight endurance and speed simultaneously.

The last two groups of the biggest unmanned aeroplanes are known as Medium Altitude Long Endurance (MALE) and

High Altitude Long Endurance (HALE). These vehicles are designed to fly at altitudes of many kilometres for an extensive

amount of time, often several days, and they are able to carry the most advanced payloads.

2.2.1.2 Unmanned Surface Vehicles (USVs)

Similarly to other groups of unmanned vehicles, USVs also vary in terms of size, capabilities and performance. Usually,

such vehicles are based on conventional boats and vessels, which are later modified and equipped with special electronic

modules enabling operations. Range and performance of the vehicles are influenced by their fuel tank capacity and

installed engines, which can vary greatly.

A separate group of vehicles use passive propulsion or energy harvesting from renewable energy sources. They are usually

light vehicles, with less than a few hundred kilogrammes, powered by waves [33], wind [34] or from solar cells. The travel

time of such vehicles is limited only by maintenance periods, and often reaches a year or more. A drawback of this type

of platforms is having very limited performance, closely associated with the weather conditions.

Underwater vehicles share some similarities with surface vehicles when not underwater, which are discussed in the next

subsection.

2.2.1.3 Autonomous Underwater Vehicless (AUVs)

AUV family members vary in terms of operational time and mission capabilities. The smallest members appropriate for

maritime scenarios, known as LAUVs, can be tens of kilogramme platforms of very limited performance and able to

operate a few tens of meters below the sea surface [35]. On the other hand, larger vehicles can weight up to several tons,

operate hundreds of hours continuously and reach several kilometres in depth [36, 37].

AUVs are used with a focus on underwater surveys and specialised work. Their relatively short endurance and speed

usually cause them to operate in the vicinity of support vessels. These vehicles are usually equipped with underwater

acoustic modems, but when they surface, similarly to USVs, they may use IEEE 802.11 radios or IridiumTM modems

to communicate. Nonetheless, communication beyond line-of-sight may be compromised due to low elevation of the

antennas. Additionally, generated data may be accessed using wet-wire connectors, which operate in water environments.

Another group of vehicles are Gliders, designed for extended, constant survey over long distances. They are equipped

with wings trimmed for underwater operations, and mechanisms for changing both their buoyancy and centre of gravity,

which makes them dive when their centre of gravity is moved forward and buoyancy is reduced. While submerging the

wings create the force that pushes the vehicle forward. When reaching a predefined depth, the buoyancy increases, the

centre of gravity moves backward and the wings’ force keeps moving the vehicle forward. Gliders are designed to cover

a few thousand kilometres of distance over several months of operation. They are used for collecting environmental data

and, due to the way they are operated, can have a scarce access to communication infrastructures [11].

Deliverable D1.1 8

2. Background in Heterogeneous Maritime Environments2.2. Technologies and Opportunities in Maritime Environments

2.2.1.4 Moored and quasi-static systems

The design of moored and quasi-static systems is usually based on floating buoys, drifting, or anchored to the sea bottom or

an ice layer [38]. They can be equipped with energy harvesting mechanisms and sensors for collecting the environmental

data. This type of systems usually has very strong power restrictions due to its expected lifetime, often counted in

years [39].

Nowadays, data collection in these systems is strongly focused on transmission through a satellite link or on manual

collection by manned expeditions. Nonetheless, the growth in use of mobile unmanned vehicles is beginning to open new

frontiers in the data collection procedures for such systems. This includes not only other surface-communication systems,

but also communication with underwater sensors and vehicles [21].

2.2.2 Communication Technologies

There are many communication technologies used in academia and industry for remote unmanned systems. In this work

the focus on communication links is focused towards the transmission of research data, assuming that command and

control data relies on dedicated links, which are usually required by existing regulations (e.g. for UAVs). Although most

manned communication systems in marine environments are based on High Frequency (HF) or Very High Frequency

(VHF) radios (e.g. voice and DSC), the use of unmanned vehicles as data-mules or relays has changed that. This section

discusses different available communication links, taking into account the medium where a vehicle may operate (e.g. air

or water).

For aerial and surface vehicles the most convenient way of exchanging information is by using electromagnetic waves.

Depending on power requirements, as well as on the transceivers design and used frequencies, connections can be charac-

terised by different performance and features. Regarding used frequencies, many of the currently available technologies,

especially those used for research, are based on license-free Industrial, Scientific and Medical (ISM) frequency bands,

slightly varying between countries. Some of the most commonly used frequencies for data transfer in unmanned vehicles

are sub-GHz ISM (433 MHz, 868 MHz and 900 MHz) and GHz ISM (2.4 GHz and 5.8 GHz) [40]. In these spectra

operate for example IEEE 802.11 or IEEE 802.15.4, as well as a great number of proprietary solutions.

The selection of appropriate communication technologies is not an easy task. With respect to the Shannon-Hartley theo-

rem, the available bit rate of a link is related to the channel’s bandwidth and Signal to Noise Ratio (SNR) [41]. For higher

frequencies, regulations allow much higher channel bandwidths, therefore better connection speed. On the other hand,

power limitations favour narrower bandwidths for long range communication – by increasing Power Spectral Density

(PSD). Moreover, the higher the frequency, the less robust the link becomes, mostly due to the relation in the increase of

signal attenuation and its reflectivity with frequency increase.

The sub-gigahertz ISM frequencies usually offer transmission rates measured in a few kb/s, while the gigahertz tech-

nologies offer bitrates from tens to hundreds of Mb/s. Therefore, in some cases, licensed transceivers with higher power

allowances, and wider bandwidths, are preferred. In addition, a licensed spectrum has better chances for low background

noise in operating frequencies, thus achieving better link quality. Depending on the implementation, transceivers may

offer Point-to-Point, or Point-to-Multipoint connections, supporting various number of clients, ad-hoc and mesh connec-

tivity, or static configurations.

For AUVs, and partially USVs, electromagnetic waves can also be used in underwater environments. However, this is not

a favourable method for data exchange in water-based environments since signal attenuation limits both range and data-

throughput. For this reason researchers and industry use more suitable technologies based on optical [27] and acoustic

signals [42]. Underwater optical wireless communications can reach high bitrates, in the order of Gb/s, while having

low-power requirements, but are limited to short transmission ranges [43]. Even though AUVs and other vehicles may

approach optical modems closely enough, the performance of these systems is influenced by turbid waters and other

factors such as temperature, pressure and salinity.

Acoustic waves for underwater communications are also influenced by water parameters and have undesirable effects

such as high latency and Doppler spread. These characteristics and a high variation of water parameters, even in limited-

size areas, require acoustic links to adopt robust transmission methods that reduce the bitrate to tens of kb/s [44] even in

short distances. Nonetheless, acoustic communications are superior to optical links for larger distances [45], being able

to transmit hundreds of bits for very long ranges (e.g. 1000 km) [43].

Deliverable D1.1 9

2. Background in Heterogeneous Maritime Environments2.2. Technologies and Opportunities in Maritime Environments

2.2.3 Networking Technologies

Computer networks are present around the world, being composed of various protocols and communication technologies,

creating an infrastructure that defines the Internet as we know it today. The ubiquitous presence of Internet-capable devices

has, in the past few years, introduced the concept of the Internet of Things (IoT), which aggregates any networking-capable

device under the assumption of common networking protocols, such as the Internet Protocol (IP).

Due to their importance, maritime environments should also be considered as part of the IoT, embracing a multitude of

different unmanned vehicles and sensors, as well as of different communication technologies. Aligned with the goals of

aggregating different devices under a “common Internet”, the Internet Engineering Task Force (IETF)6, responsible for

standardising the Internet as we know it, hosted different Working Groups (WGs) focused on handling this issue. Notable

examples of such activities are IPv6 [46] and the IPv6 over Networks of Resource-constrained Nodes (6lo) working

group 7.

The various research activities towards standardising the Internet of Things have allowed to establish a common archi-

tecture for different devices, with different capabilities, where IPv6 is seen as a convergence layer between several types

of networks (e.g. Ethernet, IEEE 802.11, Bluetooth or 3G/4G/5G, among others). Other WGs complete this common

architecture by focusing on routing aspects, such as the Routing Over Low power and Lossy networks (ROLL)8, and in

the definition of resource-oriented frameworks such as the Constrained RESTful Environments (CORE)9.

Wireless Sensor Networks (WSNs) have been used in the past not only inland but also in different maritime environ-

ments [47, 48, 49, 50], and developments on WSNs have increased significantly as a subset of IoT [51]. In fact, several

IoT communication technologies for Personal Area Networks (PANs) have been derived from WSNs, such as the IEEE

802.15.4 standard [52].

Due to the heterogeneity of applications and nodes in maritime environments, WSNs represent only a small group of

networking possibilities to be considered. Their network architectures include the concept of sink nodes, or border

routers, similarly to mesh networks, which typically act as gateways or as a backbone to the Internet. Alternatively, Mobile

Ad-hoc Networks (MANETs) have a more distributed network architecture where routing protocols do not necessarily

require nodes with particular roles [53]. MANETs have already been proposed for maritime environments [54, 55], but

the characteristics, constraints and challenges of such environments require additional considerations such as Disruptive

or Delay Tolerant Network (DTN) and different routing protocols [56, 57, 58].

When considering real-world challenges and off-the-shelf equipment, specific solutions for creating a network of hetero-

geneous vehicles in maritime environments have been developed in the past [59, 19]. Pinto et al. present a solution that

provides interoperability between different communication technologies and motivates the employment of features such

as DTN [60]. Even though this solution relies on custom protocols, it opens the possibility of creating a standardised link

to the future Internet, where IPv6 and 6LoWPAN can be used to interconnect devices with different capabilities and solve

issues such as automatic address attribution [61].

The use of IPv6 in maritime networks will allow interoperability with the Internet of Things (IoT), connecting them with

others, such as satellite networks [62], and will provide a large number of mature and standardised features that build on

IP (e.g. security and reliability). Desirable features include not only the routing and multi-hop routing between different

nodes and technologies, but also the possibility to handle distinct data flows, with specific requirements (e.g. control data

vs. payload data), in different ways [63]. For instance, traffic engineering in maritime environments allows mapping data-

flows to different technologies and routes, taking available network resources, cost and data characteristics (e.g. priority)

into account.

Regarding future network management and control, Software-Defined Networking (SDN) can be seen as a means for

simplification and evolvability of existing networking solutions [64]. The SDN approach enables a separation of the

control plane from the data plane into a central controller, which also opens new possibilities for demanding scenarios

such as maritime environments and WSNs [65]. In fact, by optimising the number of signalling messages from fully

distributed protocols, as well as by migrating computationally complex algorithms to the control plane, improvements on

scalability and energy savings may be achieved. Moreover, the SDN paradigm allows a fine-grained control of data-flows,

enabling sophisticated traffic management options such as prioritisation or multiple backup paths.

6http://ietf.org7https://datatracker.ietf.org/wg/6lo/8https://tools.ietf.org/wg/roll9https://datatracker.ietf.org/wg/core/

Deliverable D1.1 10

3 Internet of Maritime Things

The presented enabling technologies show the existing possibilities for conducting coordinated remote-sensing missions

using unmanned vehicles in maritime environments. This work suggests the integration and cooperation of different net-

working and communication technologies, together with heterogeneous vehicles, in order to allow maritime activities in

challenging environments. As shown by the described state-of-the-art scenarios, such activities benefit from the comple-

mentarity of available systems to become more efficient (i.e. cost-wise, carbon footprint, sensor precision), being capable

of dynamically adapting and scaling to different changes. For example, it was shown that the use of UAVs can leverage

research-data acquisition not only by carrying specific sensors, but also by complementing other existing ones, acting as

network relay nodes between vehicles and extending line-of-sight communications [66]. The following sections provide a

proposal for a reference maritime scenario, including a classification for different possible operation scales, as well as rec-

ommendations on how to coordinate heterogeneous unmanned vehicles, in Section 3.1. Challenges and requirements are

presented in Section 3.2, followed suggestions and future directions towards intermittent networking and communications

in Section 3.3, including a definition for different classes of communication links.

3.1 Reference Scenario

Figure 3.1: Co-existence of heterogeneous communications and vehicles

Maritime operations can be very diversified and lead to a multitude of distinct scenarios. For instance, both dense and

sparse deployments of nodes for environmental monitoring may be required. This concerns not only research-oriented

activities but also economical or safety operations.

A reference scenario that depicts some possible deployments is presented in Figure 3.1. This figure show how nodes with

different capabilities interact and enhance connectivity between themselves. Additionally, it illustrates a possible network

topology for improved performance.

The main actors to consider in this scenario are the monitoring/sensing nodes, aerial and surface vehicles, satellite nodes

and ground stations. One or more Command and Control (C&C) centres will also be part of this reference scenario,

responsible for coordinating operations. This entity is not depicted as it will likely be connected to existing inland

infrastructures, communicating directly with ground stations.

The ground stations seen in Figure 3.1 represent the edge of available communication infrastructures. These will be

Deliverable D1.1 11

3. Internet of Maritime Things 3.1. Reference Scenario

interconnected between themselves using the Internet and will provide a wireless link to other communication systems.

In particular, these ground stations will connect to satellite nodes and to vehicles that approach the shore. Additionally,

two or more ground stations should be placed taking into consideration the trajectories of satellite swarms, one in the

beginning and another at the end of the area under observation.

Small-satellites also know as SmallSats, or swarms of these nodes, are seen as a potential solution for improving com-

munications in maritime environments. This solution allows more frequent visits within a target area (instead of a more

global coverage), but with a limited communication period due to the low orbits. Since the nodes in a smallsat swarm to

not communicate between themselves, they strongly rely on ground communications. However ground stations are not

always available and efficient strategies for relaying data are required.

Vehicles travelling in the vicinities of the sensor nodes may also be used to collect data, as well as to deliver configuration

messages. The proposed approach is focused on the use of autonomous unmanned vehicles and planned visits to

sensor nodes. However, opportunistic interactions with other vehicles (e.g. transport ships), may also be used to increase

connectivity. Even though unmanned vehicles may act as relay nodes, when sufficiently close to an infrastructure, their

primary goal will be to act as data-mules.

Sensor nodes are envisaged as quasi-static nodes that aim at collecting scientific data from a given area, though mobile

nodes may exist. This area may be covered by a single node or by a cluster, where nodes may be able to communicate with

each other. The monitored data is to be relayed through multi-hop links whenever a group of clusters is close to shore.

However, most maritime operations will likely take place in remote locations. Moreover, visiting vehicles may have

limited resources that constrain their operation, not being able to reach all the nodes in one area. In this case, multi-hop

cooperation between the nodes will again be important to guarantee that all sensor nodes are reachable.

3.1.1 Maritime Remote-Sensing Areas

Maritime observations have diverse objectives that largely influence the area under study. For instance, remotely sensing

a fish farm may require the monitoring of no more than a few kilometres. However, obtaining migration data of large

animals that travel large distances, such as whales, requires a global-scale observation. Bearing such diversity in mind,

this work considers four scales of operation.

Small scale: Operations concerning the coordination of self-contained system for remote-sensing within a few

kilometres;

Medium scale: Sensing missions that can cover up to tens of kilometres;

Large scale: Initiatives that may include the cooperation of different teams and infrastructures, accounting for

hundreds of kilometres;

Global scale: Operations without fixed boundaries, typically involving several actors.

The perception of scale in a mission may result from the perspective of a single-user, or a research-team, in which

interoperability is foreseen between vehicles belonging to that action. Nonetheless, the use of standard protocols and

interfaces can allow a researcher to embark on a global-scale mission even with a limited number of vehicles. For

example, by solely deploying drifting nodes, a researcher may remotely access its sensors’ data through several other

vehicles operating around the world that are able to forward it.

The global cooperation between multiple vehicles and infrastructures may result from agreements between different par-

ties (e.g. multiple research teams), similarly to the creation of the Internet. This global perspective can be enabled by use

of standard protocols and interfaces, where the cost for forwarding nodes accounts only for energy and data-rate usage.

For some vehicles this cost may be too high and therefore they will not act as forwarding nodes, but for others it might be

negligible. For example, existing infrastructures and large vessels typically have enough resources so that they can act as

data mules to piggy-back research data (i.e. relay or forward data).

3.1.2 Coordinated Unmanned Vehicles

As previously seen, the coordinated use of multiple heterogeneous unmanned vehicles for remote-sensing can be recom-

mended for several reasons. Not only can different sensors be used to acquire data in different mediums (e.g. underwater,

surface and aerial), but they can also improve the overall communication performance. The area of operation, however,

must also be taken into consideration when selecting remote unmanned vehicles that may enhance research-data acquisi-

tion.

Deliverable D1.1 12

3. Internet of Maritime Things 3.2. Challenges and Requirements

Figure 3.2: Remote-vehicle type per scenario

Figure 3.2 illustrates how four different classes of remote vehicles – aerial, surface, stationary and underwater – fit different

scales of sensing missions. Starting with remotely operated UAVs, as presented in Section 2.2, mini-UAVs are able to

carry limited payloads and also have limited endurance and speed, which makes them suitable for small-scale missions.

Still appropriate for this scale, but robust enough (i.e. endurance and speed) to be used in medium-scale remote sensing

operations, small UAVs are able to carry heavier equipment in order to support their operation. Higher-grade UAVs, from

medium to MALE or HALE, can also be used in smaller missions but would not be cost-efficient and therefore should be

considered for larger scales, fully exploiting their endurance and speeds.

Regarding the surface of maritime environments, small or medium sized unmanned boats fit both small and medium scale

scenarios, being easier to remotely operate than large vessels while still delivering the desired performance. These boats

typically have large speeds and autonomy, but are not suited to large scale missions. In this case, other unmanned vehicles

based on renewable-energy harvesting may be able to cover significantly larger distances, even though at lower speeds,

using for instance passive propulsion. Alternatively, for large-scale or global-scale scenarios, manned or unmanned vessels

can be used, covering large distances at higher speeds, without significant autonomy issues but at a higher costs.

Stationary vehicles such as buoys have also been considered as a means to collect research data, typically used as leaf

nodes. Despite not having any propulsion mechanisms, drifting buoys can be “remotely-controlled” by deploying them in

selected and relevant currents or tides, useful in some research scenarios. Moored buoys lack the mobility aspects of other

vehicles but, by using efficient communication and networking mechanisms, research-data may also be remotely accessed

through multi-hop links or by relaying data through other vehicles. Due to the particular characteristics of buoys, which

can operate for long periods with little or no maintenance, their use is sensible in scenarios of all scales, specially when

cooperating with other vehicles.

Underwater vehicles are the last class of vehicles considered for the analysed maritime scenarios. They play a very

relevant role in data-gathering that otherwise could only be acquired through complex and dangerous manned operations.

The constraints imposed by underwater conditions limit the options available to AUVs and simpler models, such as

LAUVs. Though these provide enough autonomy and more manoeuvrability than gliders, they can only be used in small-

scale missions. Larger AUVs are heavier and more complex, not being suitable for small missions where such difficulties

are not justifiable. Indeed, they are more fitting for medium-scale operations in maritime environments, but still limited in

what concerns large and global scales, where autonomy becomes an issue. Gliders, on the other hand, are able to operate

for longer periods. Enabled by energy harvesting mechanisms, they cover large distances making them the best option for

medium to global-scale missions.

3.2 Challenges and Requirements

The challenging conditions of maritime scenarios, in addition to the lack of infrastructures and devices’ heterogeneity,

demands specific requirements capable of representing existing needs. These requirements can be divided into functional

and non-functional requirements, which should be taken into consideration according to existing technologies.

Deliverable D1.1 13

3. Internet of Maritime Things 3.3. Software-defined Intermittent Networking and Communications

3.2.1 Non-functional Requirements

The proposed system shall enable interoperability between different communication technologies, which will shall be

useful to mitigate network partitioning. In particular, it will provide multiple degrees of communication coverage and

performance.

Maritime operations are characterised for intermittent connectivity, therefore the system shall be robust and resilient

to these conditions. The system shall include delay/disruptive tolerant semantics in the network-substrate, allowing the

usage of distributed systems similar to the ones used across the Internet.

Communication shall be accessible to all nodes in a scalable fashion. Due to the heterogeneity of services and actors,

the system shall also provide distinct levels communication quality according to the priority assigned to different data

sources.

The overall solution shall be extensible in alignment with standards and protocols developed for the Internet. This will

allow maintaining an up-to-date, stable and secure system for current and future developments in maritime operations.

3.2.2 Functional Requirements

In the proposed scenario unique local IPv6 addresses must be configured for allowing local communication between

groups of nodes, which should create local network. Moreover, whenever nodes capable of connecting to the Internet

are in range of the local networks, unique global addresses should also be configured. These nodes capable of providing

Internet connectivity, shall be considered as Gateway (GW) nodes. They must be able to forward packets as relays or of

storing them locally, taking custody of data and acting as data-mules.

The interaction between the distinct types node of nodes must be structured following a hierarchical architecture

discussed. Resource-constrained devices expected to operate for long-periods of time and detached from communication

infrastructures must be considered as leaf nodes. Fixed infrastructures and powerful nodes such as large vessels must be

placed in the root of the networking hierarchy (as backbone GWs), using remotely operated nodes (e.g. UAVs, USVs) or

even satellites as intermediate gateways.

Connectivity provided by GWs must be configured through IPv6 Route Advertisements (RAs) of Global Unique Prefixes.

Additionally, multi-hop data forwarding may be used to complement the connectivity provided by RAs, which is limited

to one-hop. This may be required due to the distance between nodes and GW coverage, as well as due to possible physical

obstacles, relying on data offload to a specific leaf node.

Multi-hop routing should result from path configurations computed and triggered by the Command and Control centre.

This guarantees that only relevant paths are configured, tailored for specific purposes, and reduces signalling overhead in

scenarios with limited mobility. However, in order to allow generic multi-hop forwarding towards a GW, IPv6 Routing

Protocol for Low-Power and Lossy Networks (RPL) may also be used. In this case a GW node must act as a Border

Router and establish a Destination-Oriented DAG (DODAG) in order to collect data gathered by the sensor nodes

efficiently.

The configuration of forwarding paths must result from SDN primitives, which exploit the separation between the data

and control planes. GW nodes shall act as proxies for the controller plane, which must be connected to the Command

and Control centre, and guarantee that configuration messages are correctly delivered to sensor nodes. Moreover, traffic

differentiation must be supported by all forwarding nodes, allowing specific flow-rules to prioritise critical data and

better manage resources.

3.3 Software-defined Intermittent Networking and Communications

In the context of the most challenging maritime environments, such as the Arctic, a combination of multiple vehicles may

take part in the networking and communication systems. Upon the selection of the appropriate vehicles for each type

of mission, preferred communication channels and networking technologies can also be chosen according to the scale of

each vehicle and available resources. Additionally, the choice of communication technologies may also be influenced by

the desired bitrate to transmit research data. Nonetheless, the concept presented in this work focuses on achieving the

highest possible data-rates, without compromising the systems’ performance and overall lifetime.

Vehicles may have different tasks and available resources in a scientific sensing mission. In addition, they may also

have different roles regarding communication and networking. Taking these into consideration, three main classes of

communication links are envisaged, as depicted by Figure 3.3. These classes are:

Deliverable D1.1 14

3. Internet of Maritime Things 3.3. Software-defined Intermittent Networking and Communications

Figure 3.3: Classes of communication links

Backbone links The first class considers GHz frequencies to support “backbone links”, which are best suited

for powerful nodes capable of covering large distances. These links are resource-demanding and may require

specialised equipment (e.g. antennas), but guarantee high bitrates at large distances beyond hundreds of kilometres;

Gateway links An intermediate class that uses GHz ISM frequencies for providing “gateway links” between

different vehicles at reasonable distances (i.e. hundreds of metres), enabling them with high bit-rates (i.e. hundreds

of Mb/s) and without compromising energy efficiency. In this same class, but from an underwater perspective,

optical links can also be considered as gateways, providing high-bitrate (i.e. up to Gb/s) links, though at shorter

distances;

Local links The third class of communication links is envisaged for resource-constrained nodes, where commu-

nication follows a WSN or IoT fashion, establishing lower-bitrate connections with other nodes. For this purpose,

sub-GHz and sub-GHz ISM frequencies can be used to enable energy efficient links while covering large distances,

up to tens of kilometres. Regarding underwater communications, acoustic links have similar characteristics, pro-

viding lower data-rates than optical communication but longer ranges.

Focusing on vehicles’ characteristics and diversity, UAVs may resort to different types of communication and fit into

different classes. The main consideration is the size of the UAV and the desired coverage, which also influences the

type of mission being considered. In particular, most UAVs have privileged conditions to act as relay nodes, which makes

Medium and MALE/HALE UAVs the best candidates to provide long-range backbone links using GHz-based frequencies.

GHz ISM communications are better suited for smaller UAV models, which in small to medium scale operations, can

provide Gateway links to relay data.

When considering AUVs, underwater acoustic communications are suggested as a viable way of transmitting a small

amount of data with a considerable range (c.f. Section 2.2), suitable for PAN links. Nonetheless, using GHz ISM links

(i.e. Gateway links) provide the most efficient way of transmitting research-data when AUV resurface. Underwater optical

communications can also be used to transmit research-data at higher bit-rates, as Gateway links, but this requires shorter

ranges, which are not suitable for Gliders, and their usage has not been fully exploited yet.

The deployment of arrays of moored buoys across large distances conveys them a characteristic that stands out from other

nodes. Not only are they expected to operate over several years, but their isolation may also limit their interaction with

other vehicles. This motivates the use of long-range links for establishing WSN/IoT connections (i.e. buoy to buoy),

which are designed to not compromise resource-constrained devices’ lifetime, but that the available bitrate to the order of

kb/s. The preferred option for establishing Gateway links (i.e. high bitrate) is similar to AUVs, which should rely on GHz

Deliverable D1.1 15

3. Internet of Maritime Things 3.3. Software-defined Intermittent Networking and Communications

ISM communications above water and on optical modems when underwater, using other vehicles as relay nodes.

Large USVs, namely vessels, have more resources and, similarly to large UAVs, are able to use more energy-demanding

solutions such as GHz radios to achieve extended coverage and high bitrates. However, USVs based on energy harvesting

are expected to operate for large periods of time and cover large distances, which renders GHz solutions, together with

satellite-based communications, undesirable for relaying research data. A more energy efficient solution is to exploit

GHz ISM-based communication possibilities, exploiting Gateway links between planned or opportunistic visits of larger

vehicles or UAVs, leaving expensive links for emergency situations only.

As previously mentioned, multiple communication interfaces per node should be considered for improving performance

and interoperability between different vehicles. Regarding the use of additional radios, AUVs for example, can use both

GHz ISM-based and optical-based communications to complement low-bitrate Local links. Another example considers

USVs based on renewable energies, which should take advantage of the proximity with other vehicles, such as vessels, to

use them as relay Gateway nodes. This implies that vessels should also carry GHz ISM-based radios, providing a gateway

link in addition to the backbone communication link (GHz-based) used for transmitting their own research data.

In addition to the choice of appropriate unmanned vehicles and communication technologies for different scenarios, net-

working protocols should also be wisely chosen. The current status of IPv6 and related protocols for resource constrained

devices indicate that this solution should be preferred to other custom options. Version 6 of the Internet Protocol is widely

supported by manufacturers and is also enabled by 6LoWPAN in resource-constrained devices. This fact, as well as all

the existing protocols and security mechanisms built on top of IP, allow for more efficient data transmission and inter-

operability between different systems. As a result, the choice of protocols to be used should be dependant on available

resources (e.g. bitrate), and the use of more sophisticated techniques such as DTN or SDN may be applied in a broad

scope, according to the users’ and data needs. Moreover, these protocols are fundamental for meeting the requirements

previously identified.

Table 3.1 summarises the available options regarding the use of unmanned vehicles for different remote-sensing opera-

tions, taking into account the scale or range of the area to be surveyed. The characteristics of each vehicle influence not

only their use in specific conditions, but also the type of communication technologies that can be accommodated. For

example, underwater radio communication is deemed as not likely to be used in any of the defined scales of operation.

The chosen communication technologies represent the primary data-links per vehicle, as previously discussed, exploiting

interoperability and interactions between different devices for conserving available resources and relaying data between

more-powerful vehicles. In particular, significant resource-constraints that result from vehicles’ specifics (e.g. available

energy, typical lifetime) are illustrated by the networking and communication options with a greyed background. These

correspond to resource-constrained solutions that have limited bitrates and require specific protocols (e.g. IoT/WSN pro-

tocols), but that should still be considered to complement the unavailability of better “gateway links” (e.g. GHz-ISM or

Optical).

Deliverable D1.1 16

3. Internet of Maritime Things 3.3. Software-defined Intermittent Networking and Communications

Table 3.1: Summary of Configurations for Coordinated Remote-Sensing Systems in Maritime Environments

Deliverable D1.1 17

4 Conclusions

A reference scenario and requirements for networking and environmental remote-sensing in challenging maritime envi-

ronments has been presented. It includes the coordinated use of heterogeneous unmanned vehicles and state-of-the-art

networking solutions for achieving it. This solution creates new possibilities for acquiring research information, intercon-

necting vehicles and sensors to achieve better precision and provide continuous access to this data.

Different robotics, communication and networking concepts and challenges were considered in order to achieve a network

of unmanned vehicles, which remove the need for manual data collection and dangerous manned expeditions. This

work analyses two state-of-the-art maritime remote-sensing scenarios using UAVs and other unmanned vehicles, their

characteristics, scale, required sensors and a description of the used technologies. Both scenarios relied on different

technological options and on custom or proprietary network solutions, which rendered them incompatible and limited in

sensing possibilities.

The suggested reference scenario handles currently existing open-issues on interoperability, building on the provided anal-

ysis of state-of-the-art technologies on robotics, communication and networking for maritime environments. A correlation

between different types of unmanned vehicles and communication technologies is also explored, as well as appropriate

standardised networking protocols for interconnecting nodes according to the available resources. The conducted review

and discussion shows that existing technologies already exist, capable of supporting a heterogeneous network for challeng-

ing maritime environments, motivating the definition of an appropriate networking architecture using such technologies.

Deliverable D1.1 18

Bibliography

[1] N. Campbell, Biology: Concepts & Connections. Pearson/Benjamin Cummings, 2006. [Online]. Available:

https://books.google.com.ng/books?id=OhdFAQAAIAAJ

[2] European Union, “The blue economy of the european union,” http://ec.europa.eu/maritimeaffairs/documentation/

publications/documents/poster-blue-growth-2015 en.pdf, 2015, [Online; accessed 13-July-2016].

[3] C. Dawson, “Arctic shipping volume rises as ice melts,” http://www.wsj.com/articles/

arctic-cargo-shipping-volume-is-rising-as-ice-melts-1414612143, 2014, [Online; accessed 15-May-2016].

[4] A. Sivertsen, S. Solbø, R. Storvold, A. Tøllefsen, and K.-S. Johansen, “Automatic mapping of sea ice using un-

manned aircrafts,” in ReCAMP Flagship Workshop Book of Abstracts, ReCAMP Flagship Workshop. ReCAMP

Flagship Workshop, 2016, p. 30, n/a.

[5] A. S. Woodget, P. E. Carbonneau, F. Visser, and I. P. Maddock, “Quantifying submerged fluvial topography using

hyperspatial resolution uas imagery and structure from motion photogrammetry,” Earth Surface Processes and

Landforms, vol. 40, no. 1, pp. 47–64, 2015. [Online]. Available: http://dx.doi.org/10.1002/esp.3613

[6] A. Lucieer, D. Turner, D. H. King, and S. A. Robinson, “Using an unmanned aerial vehicle (uav) to

capture micro-topography of antarctic moss beds,” International Journal of Applied Earth Observation and

Geoinformation, vol. 27, Part A, pp. 53 – 62, 2014, special Issue on Polar Remote Sensing 2013. [Online].

Available: http://www.sciencedirect.com/science/article/pii/S0303243413000603

[7] S. Solbø, R. Storvold, A. Sivertsen, C. Petrich, and B. Sand, “Imaging sea ice structure by small remotely piloted

aircraft,” in ReCAMP Flagship Workshop Book of Abstracts, ReCAMP Flagship Workshop. ReCAMP Flagship

Workshop, 2016, p. 32, n/a.

[8] M. Faria, J. Pinto, F. Py, J. Fortuna, H. Dias, R. Martins, F. Leira, T. A. Johansen, J. Sousa, and K. Rajan, “Coordinat-

ing uavs and auvs for oceanographic field experiments: Challenges and lessons learned,” in 2014 IEEE International

Conference on Robotics and Automation (ICRA), May 2014, pp. 6606–6611.

[9] M. Ludvigsen, P. S. Dias, S. Ferreira, T. O. Fossum, V. Hovstein, T. A. Johansen, T. R. Krogstad, Ø. Midtgaard,

J. S. P. Norgren, Ø. Sture, E. Vagsholm, and A. Zolich, “Autonomous network of heterogeneous vehicles for marine

research and management,” in IEEE Oceans 2016 – Monterey, CA, September 2016.

[10] F. Py, J. Pinto, M. A. Silva, T. A. Johansen, J. Sousa, and R. K., “Europtus: A mixed-initiative controller for multi-

vehicle oceanographic field experiments,” in Int. Symp. Experimental Robotics, 2016.

[11] D. Roemmich, L. Boehme, H. Claustre, H. Freeland, M. Fukasawa, G. Goni, W. J. Gould, N. Gruber, M. Hood,

E. Kent, R. Lumpkin, S. Smith, and P. Testor, “Integrating the ocean observing system: Mobile platforms,” in

Proceedings of OceanObs’09: Sustained Ocean Observations and Information for Society. European Space

Agency, dec 2010. [Online]. Available: http://dx.doi.org/10.5270/OceanObs09.pp.33

[12] C. H. Greene, E. L. Meyer-Gutbrod, L. P. McGarry, L. C. H. Jr., S. McClatchie, A. Packer, J.-B. Jung, T. Acker,

H. Dorn, and C. Pelkie, “A wave glider approach to fisheries acoustics: Transforming how we monitor the

nation’s commercial fisheries in the 21st century,” Oceanography, vol. 27, December 2014. [Online]. Available:

http://dx.doi.org/10.5670/oceanog.2014.82

[13] M. J. Perry and D. L. Rudnick, “Observing the ocean with autonomous and lagrangian platforms and sensors: The

role of alps in sustained ocean observing systems,” Oceanography, vol. 16, no. 4, pp. 31–36, 2003, n/a.

[14] Imagenex Technologies, “Overview of the imagenex deltat sonar real time operation in an autonomous underwater

vehicle (auv) application,” 2006.

[15] M. Redmayne, “Autonomous onboard hydrographic data processing,” in OCEANS 2015 - MTS/IEEE Washington,

Oct 2015, pp. 1–4.

[16] T. A. Johansen, A. Zolich, T. Hansen, and A. J. Sørensen, “Unmanned aerial vehicle as communication relay for

autonomous underwater vehicle – field tests,” in 2014 IEEE Globecom Workshops (GC Wkshps), Dec 2014, pp.

1469–1474.

[17] A. Zolich, T. A. Johansen, K. Cisek, and K. Klausen, “Unmanned aerial system architecture for maritime missions.

Deliverable D1.1 19

Bibliography Bibliography

design hardware description,” in 2015 Workshop on Research, Education and Development of Unmanned Aerial

Systems (RED-UAS), Nov 2015, pp. 342–350.

[18] L. L. Sousa, F. Lopez-Castejon, J. Gilabert, P. Relvas, A. Couto, N. Queiroz, R. Caldas, P. S. Dias, H. Dias, M. Faria,

F. Ferreira, A. S. Ferreira, J. Fortuna, R. J. Gomes, B. Loureiro, R. Martins, L. Madureira, J. Neiva, M. Oliveira,

J. Pereira, J. Pinto, F. Py, H. Queiros, D. Silva, P. Sujit, A. Zolich, T. A. Johansen, J. Sousa, and K. Rajan, “Integrated

monitoring of mola mola behaviour in space and time,” PLoS ONE, 2016.

[19] J. Pinto, P. S. Dias, R. Martins, J. Fortuna, E. Marques, and J. Sousa, “The lsts toolchain for networked vehicle

systems,” in OCEANS - Bergen, 2013 MTS/IEEE, June 2013, pp. 1–9.

[20] G. Ocean, “Ocean tracking network global metadata and data atlas,” http://members.oceantrack.org/data/discovery/

GLOBAL.htm, 2016, [Online; accessed 13-July-2016].

[21] A. Zolich, J. A. Alfredsen, T. A. Johansen, and K. R. Skøien, “A communication bridge between underwater sen-

sors and unmanned vehicles using a surface wireless sensor network - design and validation,” in OCEANS 2016 –

Shanghai, April 2016, pp. 1–9.

[22] N. Beauchamp, “Return of the intrepid wave glider,” http://oceantrackingnetwork.org/

return-of-the-intrepid-wave-glider/, 2014, [Online; accessed 13-July-2016].

[23] J. Berge, “Arctic ocean ecosystems – applied technology, biological interactions and consequences in an era of

abrupt climate change (arctic abc),” http://www.mare-incognitum.no/files/downloads mnight/Jrgen Berge Oban

2015 ArcticABC.pdf, 2015, [Online; accessed 13-July-2016].

[24] T. I. Fossen, “Project 5 -– autonomous aerial systems for marine monitoring and data collection,” http://www.ntnu.

edu/amos/project-5, 2016, [Online; accessed 13-July-2016].

[25] M. L. Domeier, “Methods for the deployment and maintenance of an acoustic tag tracking array: An example from

california’s channel islands,” Marine Technology Society Journal, vol. 39, no. 1, pp. 74–80, mar 2005. [Online].

Available: http://dx.doi.org/10.4031/002533205787521668

[26] G. Warwick, “Hybrid vtol uavs – back to the future,” http://aviationweek.com/blog/hybrid-vtol-uavs-back-future,

2014, [Online; accessed 13-July-2016].

[27] I. Vasilescu, K. Kotay, D. Rus, M. Dunbabin, and P. Corke, “Data collection, storage, and retrieval with an under-

water sensor network,” in In Proceedings of the International Conference on Embedded Networked Sensor Systems

(ACM SenSys 2005, 2005.

[28] A. Martins, J. M. Almeida, H. Ferreira, H. Silva, N. Dias, A. Dias, C. Almeida, and E. P. Silva, “Autonomous

surface vehicle docking manoeuvre with visual information,” in Proceedings 2007 IEEE International Conference

on Robotics and Automation, April 2007, pp. 4994–4999.

[29] J. Palmer, N. Yuen, J. P. Ore, C. Detweiler, and E. Basha, “On air-to-water radio communication between uavs and

water sensor networks,” in 2015 IEEE International Conference on Robotics and Automation (ICRA), May 2015,

pp. 5311–5317.

[30] R. Barbatei, A. Skavhaug, and T. A. Johansen, “Acquisition and relaying of data from a floating wireless sensor node

using an unmanned aerial vehicle,” in Unmanned Aircraft Systems (ICUAS), 2015 International Conference on, June

2015, pp. 677–686.

[31] Blyenburgh & Co., “Rpas, remotely piloted aircraft systems – the global perspective 2015/2016,” http://uvs-info.

com/index.php/component/flippingbook/book/25-rpas-yearbook-2015-13th-edition-bis/3-about, 2015, [Online; ac-

cessed 13-July-2016].

[32] European Aviation Safety Agency, “Proposal to create common rules for operating drones in europe,” https://www.

easa.europa.eu/system/files/dfu/205933-01-EASA Summary%20of%20the%20ANPA.pdf, 2015, [Online; accessed

13-July-2016].

[33] Liquid Robotics, “How it works,” http://www.liquid-robotics.com/platform/how-it-works/, 2016, [Online; accessed

13-July-2016].

[34] Offshore Sensing AS, “Sailbuoy – unmanned ocean vessel,” http://www.sailbuoy.no/, 2016, [Online; accessed 13-

July-2016].

[35] LSTS, “Light autonomous underwater vehicle,” http://lsts.fe.up.pt/vehicles/lauv, 2016, [Online; accessed 13-July-

2016].

[36] Kongsberg, “Autonomous underwater vehicle – the hugin family,” https://www.km.kongsberg.com/ks/web/

nokbg0397.nsf/AllWeb/A6A2CC361D3B9653C1256D71003E97D5/file/HUGIN Family brochure r2 lr.pdf?

OpenElement, 2016, [Online; accessed 13-July-2016].

Deliverable D1.1 20

Bibliography Bibliography

[37] J. Ferguson, A. Pope, B. Butler, and R. Verrall, “Theseus auv - two record breaking missions,” Sea Technology,

vol. 40, no. 2, pp. 65–70, 1999.

[38] J. Berge, M. Geoffroy, G. Johnsen, F. Cottier, B. Bluhm, and D. Vogedes, “Ice-tethered observational platforms in

the arctic ocean pack ice,” in IFAC Conference on Control Applications in Marine Systems, 2016.

[39] D. Roemmich, G. C. Johnson, S. Riser, R. Davis, J. Gilson, W. B. Owens, S. L. Garzoli, C. Schmid, and

M. Ignaszewski, “The argo program: Observing the global ocean with profiling floats,” Oceanography, vol. 22,

June 2009. [Online]. Available: http://dx.doi.org/10.5670/oceanog.2009.36

[40] M. Loy, R. Karingattil, and L. Williams, “Ism-band and short range device regulatory compliance overview,” http:

//www.ti.com/lit/an/swra048/swra048.pdf, 2005, [Online; accessed 13-July-2016].

[41] D. Torrieri, Principles of Spread-Spectrum Communication Systems, 2nd Edition. Springer, 2011. [Online].

Available: http://www.amazon.com/Principles-Spread-Spectrum-Communication-Systems-2nd/dp/1441995943%

3FSubscriptionId%3D0JYN1NVW651KCA56C102%26tag%3Dtechkie-20%26linkCode%3Dxm2%26camp%

3D2025%26creative%3D165953%26creativeASIN%3D1441995943

[42] E. M. Sozer, M. Stojanovic, and J. G. Proakis, “Underwater acoustic networks,” IEEE Journal of Oceanic Engineer-

ing, vol. 25, no. 1, pp. 72–83, Jan 2000.

[43] H. Kaushal and G. Kaddoum, “Underwater optical wireless communication,” IEEE Access, vol. 4, pp. 1518–1547,

2016.

[44] M. Stojanovic, “Low complexity ofdm detector for underwater acoustic channels,” in OCEANS 2006, Sept 2006, pp.

1–6.

[45] F. Mosca, G. Matte, V. Mignard, and M. Rioblanc, “Low-frequency source for very long-range underwater commu-

nication,” in 2013 OCEANS - San Diego, Sept 2013, pp. 1–5.

[46] S. Deering and R. Hinden, “Internet Protocol, Version 6 (IPv6) Specification,” RFC 2460, Dec. 1998. [Online].

Available: https://rfc-editor.org/rfc/rfc2460.txt

[47] G. Xu, W. Shen, and X. Wang, “Marine environment monitoring using wireless sensor networks: A systematic

review,” in 2014 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Oct 2014, pp. 13–18.

[48] H. Luo, K. Wu, Z. Guo, L. Gu, and L. M. Ni, “Ship detection with wireless sensor networks,” IEEE Transactions on

Parallel and Distributed Systems, vol. 23, no. 7, pp. 1336–1343, July 2012.

[49] T. Wehs, M. Janssen, C. Koch, and G. von Colln, “System architecture for data communication and localization

under harsh environmental conditions in maritime automation,” in IEEE 10th International Conference on Industrial

Informatics, July 2012, pp. 1252–1257.

[50] D.-T. Ho, E. I. Grøtli, P. B. Sujit, T. A. Johansen, and J. B. Sousa, “Optimization of wireless sensor network and uav

data acquisition,” Journal of Intelligent & Robotic Systems, vol. 78, no. 1, pp. 159–179, 2015. [Online]. Available:

http://dx.doi.org/10.1007/s10846-015-0175-5

[51] S. S. P and S. S. Kumar, “Sea water quality monitoring using smart sensor network,” in 2015 International Con-

ference on Control, Instrumentation, Communication and Computational Technologies (ICCICCT), Dec 2015, pp.

804–812.

[52] M. R. Palattella, N. Accettura, X. Vilajosana, T. Watteyne, L. A. Grieco, G. Boggia, and M. Dohler, “Standardized

protocol stack for the internet of (important) things,” IEEE Communications Surveys Tutorials, vol. 15, no. 3, pp.

1389–1406, Third 2013.

[53] D. Palma and M. Curado, Resource Management in Mobile Computing Environments. Cham: Springer

International Publishing, 2014, ch. Scalable Routing Mechanisms for Mobile Ad Hoc Networks, pp. 65–114.

[Online]. Available: http://dx.doi.org/10.1007/978-3-319-06704-9 4

[54] R. J. Mohsin, J. Woods, and M. Q. Shawkat, “Density and mobility impact on manet routing protocols in a maritime

environment,” in Science and Information Conference (SAI), 2015, July 2015, pp. 1046–1051.

[55] Y. Kim, J. Kim, Y. Wang, K. Chang, J. W. Park, and Y. Lim, “Application scenarios of nautical ad-hoc network for

maritime communications,” in OCEANS 2009, 2009.

[56] L. Lambrinos, C. Djouvas, and C. Chrysostomou, “Applying delay tolerant networking routing algorithms in mar-

itime communications,” in World of Wireless, Mobile and Multimedia Networks (WoWMoM), 2013 IEEE 14th Inter-

national Symposium and Workshops on a, June 2013, pp. 1–6.

[57] H.-M. Lin, Y. Ge, A.-C. Pang, and J. Pathmasuntharam, “Performance study on delay tolerant networks in maritime

communication environments,” in IEEE OCEANS, 2010.

Deliverable D1.1 21

Bibliography Bibliography

[58] R. Martins, “Disruption/delay tolerant networking with low-bandwidth underwater acoustic modems,” in 2010

IEEE/OES Autonomous Underwater Vehicles, 2010.

[59] J. Pinto, P. Calado, J. Braga, P. Dias, R. Martins, E. Marques, and J. B. Sousa, “Implementation of a control ar-

chitecture for networked vehicle systems,” in IFAC Workshop on Navigation, Guidance and Control of Underwater

Vehicles (NGCUV), 2012.

[60] J. Pinto, P. Calado, J. Braga, P. Dias, R. Martins, E. Marques, and J. Sousa, “3rd ifac workshop on

navigation, guidance and control of underwater vehicles implementation of a control architecture for networked

vehicle systems,” IFAC Proceedings Volumes, vol. 45, no. 5, pp. 100 – 105, 2012. [Online]. Available:

http://www.sciencedirect.com/science/article/pii/S1474667016305870

[61] D. T. Narten, T. Jinmei, and D. S. Thomson, “IPv6 Stateless Address Autoconfiguration,” RFC 4862, Oct. 2015.

[Online]. Available: https://rfc-editor.org/rfc/rfc4862.txt

[62] M. D. Sanctis, E. Cianca, G. Araniti, I. Bisio, and R. Prasad, “Satellite communications supporting internet of remote

things,” IEEE Internet of Things Journal, vol. 3, no. 1, pp. 113–123, Feb 2016.

[63] D. Kidston and T. Kunz, “Challenges and opportunities in managing maritime networks,” IEEE Communications

Magazine, vol. 46, no. 10, pp. 162–168, 2008.

[64] S. Costanzo, L. Galluccio, G. Morabito, and S. Palazzo, “Software Defined Wireless Networks: Unbridling SDNs,”

in 2012 European Workshop on Software Defined Networking (EWSDN), Oct. 2012, pp. 1–6.

[65] L. Galluccio, S. Milardo, G. Morabito, and S. Palazzo, “Sdn-wise: Design, prototyping and experimentation of

a stateful sdn solution for wireless sensor networks,” in 2015 IEEE Conference on Computer Communications

(INFOCOM), April 2015, pp. 513–521.

[66] V. E. Hovstein, A. Sægrov, and T. A. Johansen, “Experiences with coastal and maritime uas blos operation with

phased-array antenna digital payload data link,” in Unmanned Aircraft Systems (ICUAS), 2014 International Con-

ference on, May 2014, pp. 261–266.

Deliverable D1.1 22

The SINet project

October 31, 2016

SINet-D1.1-1.0/1.0

Horizon 2020

This work is licensed under a Creative Commons “Attribution-ShareAlike 3.0 Unported”

license.