###### Abstract

As a result of the progressive implantation of the Industry 4.0 paradigm, many
industries are experimenting a revolution that shipyards cannot ignore.
Therefore, the application of the principles of Industry 4.0 to shipyards are
leading to the creation of Shipyards 4.0. Due to this, Navantia, one of the
ten largest shipbuilders in the world, is updating its whole inner workings to
keep up with the near-future challenges that a Shipyard 4.0 will have to face.
Such challenges can be divided into three groups: the vertical integration of
production systems, the horizontal integration of a new generation of value
creation networks, and the re-engineering of the entire production chain,
making changes that affect the entire life cycle of each piece of a ship.
Pipes, which exist in a huge number and varied typology on a ship, are one of
the key pieces, and its monitoring constitutes a prospective cyber-physical
system. Their improved identification, traceability, and indoor location, from
production and through their life, can enhance shipyard productivity and
safety. In order to perform such tasks, this article first conducts a thorough
analysis of the shipyard environment. From this analysis, the essential
hardware and software technical requirements are determined. Next, the concept
of smart pipe is presented and defined as an object able to transmit signals
periodically that allows for providing enhanced services in a shipyard. In
order to build a smart pipe system, different technologies are selected and
evaluated, concluding that passive and active RFID (Radio Frequency
Identification) are currently the most appropriate technologies to create it.
Furthermore, some promising indoor positioning results obtained in a pipe
workshop are presented, showing that multi-antenna algorithms and Kalman
filtering can help to stabilize Received Signal Strength (RSS) and improve the
overall accuracy of the system.

###### keywords:

IoT; RFID; Wi-Fi; Industry 4.0; Shipyard 4.0; smart pipes; identification;
localization; tracking; indoor positioning; cyber-physical systems; RSS;
multi-antenna techniques; Kalman filter.

0000 10.3390/—— xx Academic Editor: name Received: date ; Accepted: date ;
Published: date 100 Smart Pipe System for a Shipyard 4.0 Paula Fraga-Lamas,
Diego Noceda-Davila, Tiago M. Fernández-Caramés, Manuel A. Díaz-Bouza, Miguel
Vilar-Montesinos Correspondence<EMAIL_ADDRESS>Tel.: +34 981167000 x
6051

##  1 Introduction

After the triumph of the lean production systems in the 1970s, the outsourcing
manufacturing phenomenon of the 1990s, and the automation that took off in the
2000s, the fourth major disruption in modern manufacturing is Industry 4.0.
This industrial revolution can be defined as the next phase in the
digitalization of the sector McKinsey2015 , driven by several emerging
technologies: the ubiquitous use of sensors, the stunning rise in data volume,
the increasing computational power, and connectivity; the emergence of
analytics, cloud computing and business-intelligence capabilities; new forms
of human-machine interaction such as augmented-reality systems; and advances
in transferring digital instructions to the physical world, such as Cyber-
Physical Systems (CPS), Internet of Things (IoT), robotics, and 3-D printing.
Most of these technologies are mature and they have been present for some
time. Although some of them are not yet ready for a broader application, many
are now at a position where their greater reliability and cost-effectiveness
are starting to be appealing for industrial applications.

In the short-term, Industry 4.0 is expected to have a major effect on global
economies. PwC’s 2016 Global Industry 4.0 Survey PwC  suggests that annual
digital investments are expected to achieve US $907 bn per year through 2020.
Survey respondents anticipate that those investments will lead to US $493 bn
in additional revenues annually. Furthermore, savings are estimated at US $421
bn in costs and efficiency gains each year.

The foundations of the Industry 4.0 can be transferred straight to a Shipyard
4.0. The deployment of Cyber-Physical Systems in production systems gives
birth to the "smart factory" and, analogously, to the "smart shipyard".
Products, resources, and business and engineering processes are deeply
integrated making production operate in a flexible, efficient, and green way
with constant real-time quality control, and cost advantages in comparison
with traditional production systems. Machinery and equipment will have the
ability to improve processes through self-optimization and autonomous
decision-making. Shipbuilders face the same challenges as industry Wang2016 ,
which can be classified into three main concerns: the vertical integration of
production systems, the horizontal integration of a new generation of networks
that create added-value, and the acceleration of technologies that require the
re-engineering of the entire production chain.

The vertical integration of production systems changes naval production
chains. It entrusts the intelligent shipyards to ensure safe production. The
smart ships, more environmental friendly, are capable of operating in network
together with other ships and ground infrastructure. The horizontal
integration of a new generation of value creation networks is critical as it
provides an integrated way to satisfy the demands from the different
stakeholders, allowing for the customization of ships in a short period of
time.

The third challenge is the end-to-end digital integration of engineering
across the entire value chain, ranging from design to after-sales service.
This evolution implies introducing disrupting technologies that affect the
entire life cycle of each piece of the ship, from acceleration technologies,
such as artificial intelligence, robotics, virtual reality, driverless
vehicles for the transport of parts, drones, remote sensing networks or 3D/4D
printing, among others. The aim of these technologies is, primarily, to allow
shipyards to collect more data and make better use of it, for example:

  * •

Naval Command, Control, Communications, Computers, Intelligence, Surveillance
and Reconnaissance (C4ISR) capabilities will be impacted by the development of
a number of technologies based on the information extracted from the emerging
data.

  * •

Curved 3D organic light emitting diode (OLED) displays will be supported by
form factors that take advantage of capabilities such as voice, handwriting,
touch, gesture, eye movement, or even brain control. Designers will be able to
interact with their designs without a keyboard or mouse, Human-Computer
Interfaces (HCI) will encourage innovation and efficient design workflow. Such
interfaces will be able to support more natural modes of interaction and will
be more intuitive and therefore easier to operate, reducing the need for
training.

  * •

Data obtained from remote sensing and intelligent algorithms will accelerate
the ship design process, and 2D design will be easily converted into 3D.

  * •

Complex construction and inspection tasks will be supported by augmented
reality.

  * •

Graphene strips, with allocated sensors alongside the hull, will provide more
accurate data about the hull’s working conditions. These will monitor external
(seawater temperature, impacts, and fouling) and internal factors (stresses,
microbial induced corrosion, and bending). This information will enable a new
approach called Hull-Skin-Data centered decisions that would be adopted
according to those working parameters.

  * •

An increasing number of embedded sensors will be fitted to pipes so that new
laser technologies and robotics can speed up the cutting process. Adaptable
hull forms will be developed to tackle better different speed profiles and
changing load conditions. Robots will also control the curvature of materials
more precisely, thus offering optimal hull form. Moreover, a ballast free
design will be further developed to reduce the transfer of marine invasive
species across different waters.

  * •

Instead of leaving the majority of outfitting tasks until the moment after
launching, some outfitting, such as piping and heavy machinery, will be
developed together with the hull structure speeding up the building process.

  * •

Progressive sensorization process will enable automated casting, forging,
rolling, cutting, welding or cleaning cleaning .

  * •

Time spent on the outfitting along the quay will be minimized. Robotics will
capture 3D images throughout the vessel and will establish a reference dataset
to support real-time ship operations and life maintenance.

  * •

Enhanced crane-lifting capabilities will speed up production time.

Furthermore, with the development of applications based on these emerging
technologies, a Shipyard 4.0 can leverage smarter energy consumption, greater
inbound/outbound logistics and information storage (asset utilization,
supply/demand match, inventories, time to market), workforce safety and
control (automation of knowledge work, digital performance management, human-
robot collaboration, remote monitoring) and real-time yield optimization.

Navantia Navantia  is a Spanish naval company that offers integral solutions
to its clients and which has the capacity required to assume responsibility
over any naval program in the world, delivering fully operational vessels, and
support throughout the service life of the product. Its main working areas are
the design and construction of hi-tech military and civil vessels, the design
and manufacturing of control and combat systems, overhauls and alterations of
military and civil vessels, diesel engine manufacturing, and turbine
manufacturing. Although Navantia has developed naval programs all around the
world, at a domestic scale, Navantia’s main customer is the Spanish Navy (this
collaboration dates back 250 years). The high level of the Spanish Navy, with
a worldwide operating capacity and collaborations with the most modern navies,
allows Navantia to offer value added products. Specifically, this article
reviews the advances in one of the research lines of the Joint Research Unit
Navantia-UDC (University of A Coruña).

Pipes are a key part of ships: a regular ship contains between 15.000 and
40.000 pipes, whose use goes from fuel transportation or coolant for engines,
to carry drinking water or waste. With such a huge number and varied typology,
it is important to maintain the traceability and status of the pipes, what
speeds up their maintenance procedures, accelerates locating them, and allows
for obtaining easily their characteristics when building and installing them.

This need for controlling and monitoring pipes can be approached by Cyber-
Physical Systems (CPSs). A smart pipe system is a novel example of the
benefits of CPS, providing a reliable remote monitoring platform to leverage
environment, safety, strategic and economic benefits. While the physical plane
focuses on the designs for sensing, data-retrieving, event-handling,
communication, and coverage problems, the cyber plane focuses on the
development of cross-layered and cross-domain intelligence from multiple
environments and the interactions between the virtual and the physical world.
In this paper, the physical plane is based on the concept of smart pipe, a
sort of pipe able to transmit signals periodically that allows for providing
useful services in a shipyard.

Today, the pipe management process varies depending on the shipyard, but, in
general, it is performed in three different scenarios: the pipe workshop where
they are built, and the block outfitting and the ship, where assembly takes
place. This paper is focused on the pipe workshop, which is handled in a
similar way in most shipyards.

In Figure 1 it is represented the floor map of the pipe workshop that Navantia
owns in Ferrol (Galicia, Spain). The areas colored represent the main
operative areas, while in white are offices and other secondary auxiliary
areas. The following are the most relevant areas:

  * •

Pipe reception. In this area raw pipes are stacked by the suppliers. It is
divided into two different areas: small pipes are stored in a robotic storage,
while large pipes are placed on the floor on diverse spots.

  * •

Cutting. This is where pipes are cut according to the engineering
requirements.

  * •

Bending. Some pipes need to be bent to adapt them to the characteristics of
the place where they will be installed on the ship.

  * •

Manufacturing. These are actually three areas of the workshop where operators
add accessories and where pipes made of multiple sub-pipes are joined.

  * •

Provider’s outbound storage. In times of excessive production load, some is
derived to external providers. The outbound storage area is where providers
collect the pipes and return them after their processing.

  * •

Welding. There are different booths where operators carry out welding tasks.

  * •

Cleaning. Before manufacturing, pipes have to be cleaned. This area contains
bathtubs to expose pipes to hot water, acids, or pressurized water.

  * •

Main warehouse. This is where accessories and tool supplies are stored.

Figure 1: Floor map of the workshop.

In this scenario, the way pipes are currently built (detailed next in Section
2.1) can be improved and optimized. In this article it is proposed a system of
smart pipes that avoids paperwork and automates pipe identification, tracking,
and traceability control. The system consists of a network of beacons that
collect information about the location of the pipes continuously. Such
information is provided by RFID tags that also contain information that allows
operators to identify each pipe and determine how to process it at every
stage.

The present paper is aimed at applying the latest research and the best
technologies to build a smart pipe system for a shipyard, but it also includes
the following novel contributions, which, as of writing, have not been found
together in the literature:

  * •

It presents the concept of Shipyard 4.0, which consists in the application of
the principles of Industry 4.0 to a shipyard.

  * •

It describes in detail how a shipyard pipe workshop works and what are the
requirements for building a smart pipe system.

  * •

The paper indicates how to build a positioning system from scratch in an
environment as harsh in terms of communications as a shipyard. Furthermore, it
was not found in the literature any practical analysis on the application of
RFID technology in any similar application and scenario.

  * •

It defines the concept of smart pipe and shows an example of its
implementation and the architecture that supports it.

  * •

The article proposes the use of spatial diversity techniques to stabilize
Received Signal Strength (RSS) values, a kind of technique whose application
in RFID systems has not been found in the literature.

The remainder of this paper is organized as follows. Section 2 describes the
process of pipe manufacturing in a modern shipyard and analyzes the
technologies that can be used for identifying pipes. Section 3 details the
design of the system, including the operational and hardware requirements, and
the communications architecture. Section 4 reviews the system modules and the
RSS stabilization techniques proposed. Section 5 describes the experimental
setup and the tests performed with the technologies selected. Finally, Section
6 is devoted to the conclusions.

##  2 Related Work

###  2.1 Pipe manufacturing in a modern shipyard

The current procedure for managing the pipes in the workshop consists of the
following steps:

  1. 1.

Initially, pipes are placed in a storage area, where they will be collected by
operators according to production needs. In the case of the shipyard that
Navantia owns in Ferrol, two zones can be distinguished: one for small pipes
and another for the large ones. The area for small pipes is an intelligent
warehouse where an operator registers the pipes that arrive and then extracts
them on demand according to the characteristics specified. Figure 2 shows the
stacking area for large pipes, whose occupancy level is not determined
automatically.

Figure 2: Stacking area for large pipes. Figure 3: Cutting area of the
workshop.

  2. 2.

The first pipe processing point is the cutting area (in Figure 3). In
production, as soon as the first cut of a pipe is made, operators place a
plastic label that is attached using electric cable (this kind of cable is
used because it has to resist being exposed to acids and hot water). This
label contains alphanumeric identification information and includes a barcode.
Pipes are stacked on pallets, which allow for moving them easily between the
different stages of the production chain. Regarding such pallets, it is
important to note that:

     * •

Operators distinguish visually each pallet through an identifier painted on
it.

     * •

Pallets are moved by cranes through the workshop. They are not usually moved
until they are considered to be full. When a pallet is moved to a new section,
pipes are checked by operators who, by reading the label barcode with a
scanner, get information on the process that should be carried out on the
pipe. At the same time, the barcode reading operation allows for registering
its location, since every scanner is associated with a specific place.

     * •

Each pallet carries paper documentation related to the pipes contained.

  3. 3.

The second stage of the pipes is bending (if it is required). There are three
benders in the workshop, which can be controlled from a Windows-based PC that
is also able to receive and load design files from the engineering department.

  4. 4.

Before manufacturing, pipes might need to be cleaned. For such a purpose,
there is an area for degreasing and rinsing pipes by using water or certain
acids.

  5. 5.

Next, pipes are moved to the manufacturing area, where accessories are added.
These elements are transported in metal pallets from the workshop warehouse.
There is not a quick communication between the warehouse and manufacturing to
indicate when the accessories associated with a pipe are available (i.e.
operators have to walk to the warehouse and check the availability of the
accessories).

  6. 6.

After manufacturing, pipes are packed with others on pallets. This packaging
is registered before the pallet leaves the manufacturing area.

  7. 7.

Large pipes can be stored temporarily in a reserved area located at one end of
the workshop. Although there are more stacking areas, both indoors and
outdoors, there is no real-time control of the occupancy percentage of the
areas (i.e. the number of pipes in them).

  8. 8.

Next to the temporary storage, there is an area of welding stations with
plastic separations and other auxiliary areas, mainly dedicated to store
pipes.

  9. 9.

Once the pallet leaves the production area, the traceability of the pipes is
lost, and there are no records of their movements and/or location in the
different storage areas. The largest storage area is outdoors, next to the
workshop, in a nearby dock (as it is shown in Figure 4).

Figure 4: External storage areas in the dock.

###  2.2 Identification, tracking and location systems for shipyards and smart
manufacturing

In recent years, several authors have studied and proposed various
alternatives that address tasks in ship construction, including hull blasting
Faíñaet al. (May 2009) and welding Kuss et al. (June 2016); Mun et al. (July
2015); Lee (2011), that can be improved through the application of
technological solutions. For example, Kim et al. Kim et al. (Oct. 2001)
propose an automated welding machine for shipyards, in which mobile robots use
neural networks to recognize the work environment. Similarly, the same authors
propose the use of smart robots for welding in a shipyard Kim et al. (Nov.
2000), but in this case they design a display system for the recognition of
the areas to be welded.

The problem of locating people in a shipyard has been studied by Kawakubo et
al. in Kawakubo et al. (Jan. 2006). In such a paper, the authors use Bluetooth
technology for the location by means of fixed and mobile stations. Thus, the
authors achieve a precision of 1.2 m using a fixed network of readers in which
each of the readers is placed at a distance of about 8 meters.

Sensor networks have also been proposed for monitoring different construction
tasks Perez (2014). For instance, a practical example of a real-time
monitoring system for the concentration of CO is described in Yang (2015). A
more specific development for the construction of ships and maritime platforms
in a shipyard is detailed in do Amaral Bichet et al. (Mar. 2013). There, the
authors describe a system of hyper-environments that use sensor networks,
virtual reality, and RFID to improve the process of supply tacking.

In environments where the presence of metals is high, radio frequency (RF)
communications are clearly affected. This impact is well illustrated in Wong
et al. (Dec. 2013), where a series of experiments with diverse tags showed
that the signal strength decays when the tags are placed on a copper metal
plate. In this regard, in Deavours (Apr. 2010) several techniques are analyzed
to improve the performance of RFID tags on metal, showing that the length of
the antenna is a variable that can improve impedance adaptation. In an
environment close to the shipyard, the authors in Arumuganet al. (Apr. 2008)
analyze the feasibility of adhering passive RFID tags on metal bent pipes.

In order to overcome harsh environments, multiple tags and components have
been designed to enable RFID communications in metallic environments. Examples
are Raoet al. (Dec. 2010), Bovelliet al. (Sept. 2006) or Jeonget al. (Oct.
2011), where UHF RFID tags are specifically designed to be used on various
metal surfaces and containers. If conditions such as high temperatures are
added to the presence of metals, RF communication is even more complicated.
Therefore, components need to be adapted to harsh communications scenarios. An
example is studied in Heisset al. (June. 2013), where the authors analyze some
of the complications faced by hardware in the complicated conditions
mentioned, such as data memory retention for long periods of time.

###  2.3 Technologies for Identifying Pipes

This subsection analyzes different technologies that can perform pipe
identification and monitoring in a workshop. Only the most relevant tag-based
identification technologies are cited, but other approaches (e.g. dead
reckoning or image-based technologies) are available. The technologies
selected are described briefly to indicate their general characteristics,
before being analyzed and compared in Section 3.3.1. A summary of the basic
characteristics of the technologies is shown in Table 1.

Table 1: Main characteristics of the identification technologies selected.

Technology | Frequency band | Range | Features | Popular Applications
---|---|---|---|---
Barcode/QR | - | < 4 m | LOS, very low cost, visual decoding | Asset tracking and marketing
LF RFID | 30-300 KHz (125 KHz) | 1-5 cm (< 10 cm) | N-LOS, durability, low cost | Smart Industry and security access
HF RFID | 3-30 MHz (13.56 MHz) | 30 cm (< 1 m) | N-LOS, durability, low cost | Smart Industry and asset tracking
UHF RFID | 30 MHz-3 GHz | 10 m | N-LOS, durability, low cost | Smart Industry and toll roads
NFC | 13.56 MHz | 4-10 cm (< 20 cm) | Low cost, no power | Ticketing and payments
BLE | 2.4 GHz | < 50 m | Low power | Wireless headsets
Wi-Fi | 2.4-5 GHz | < 100 m | High-speed, ubiquity | LAN, internet access, broadband
Infrared (IrDA) |  800 to 1000 $\mu$ m | < 1 m | Security, high-speed | Remote control, data transfer
UWB | 3.1 to 10.6 GHz | < 10 m | Low power, high-speed data | Radar, video streaming
Ultrasound | >20 kHz (2-10 MHz) | <3 m | Inspection of industrial materials | Medicine, positioning and location
ZigBee | 868 MHz (EU), 2.4 GHz | < 10 m | Mesh network | Smart Home and Industry
DASH7 | 315-915 MHz | <5 Km | BLAST network technology | Smart industry and military
ANT+ | 2.4 GHz | < 10 m | Low power | Health, sport monitoring
Z-Wave | 868 MHz (EU) | < 30 m | Simple protocol | Smart Home
WirelessHART | 2.4 GHz | < 10 m | HART protocol | Smart Industry
LoRa | 2.4 GHz | > 15 m | Long battery life and range | Smart city, M2M
SigFox | 868 MHz | 3-50 Km | Global cellular | Internet of Things, M2M
RuBee | 131 KHz | 1-30 m (15 m) | Harsh environments | Mission-critical scenarios

Navantia’s current pipe monitoring system is based on barcodes, which
represent a set of parallel lines of different thickness and spacing that, as
a whole, contain certain information. Barcode readers are devices that
translate optical impulses into electrical signals, so it is essential to
place the code so that good visibility and readability are achieved. The usual
reading distance is tens of centimeters, although there is specialized
equipment that can reach several meters.

There are two types of barcodes: linear and two-dimensional codes. Linear
codes represent alphanumeric information (e.g. Code 39, Code 93) or numbers
(EAN, European Article Number). Two-dimensional codes are able to encode more
information per unit of area than linear codes. An example of two-dimensional
codes are QR (Quick Response) codes, which were originally designed for the
Japanese automotive industry. The code consists of black squares distributed
through a grid with white background that can be read by an optical device.
Large boxes at the corners allow to detect the code position, having a fourth
one for the alignment and orientation.

QR code reading distance depends on the size of the code: increased distances
are obtained thanks to increasing QR code size in proportion. It is usually
assumed that code size has to be one tenth of the reading distance (for
instance, if a code has to be read at about 20 meters, it should have a size
of at least 2 meters). Regarding the storage capacity of a QR code, it depends
on the type of data encoded, the version, and the error correction level.

Besides the automotive industry, it is easy to find nowadays QR codes applied
in other fields. For example, a QR code traceability system for mitigating
food supply chain risks is described in QR (2014).

Another identification technology that has experienced a huge growth in the
last years is RFID (Radio Frequency Identification). Such a technology
consists of readers and electronic tag (also called transponders). These tags
are very low power components that react to waves emitted by radio readers by
providing the stored information.

RFID systems are usually classified according to two characteristics: their
frequency of operation and the way they are powered. Depending on the
frequency, RFID systems may be classified in radio bands. Each band
differences from the others in its propagation behavior and spectrum
regulations. There are three main RFID bands:

  * •

LF (Low Frequency) RFID. According to the ITU (International
Telecommunications Union), the LF band goes between 30 KHz and 300 KHz.
Frequency and power in this band are not regulated globally in the same way:
most systems operate at 125 KHz, but there are some at 134 KHz. The reading
range provided is short (between 1 and 5 cm and, generally, up to 10 cm), so,
in practice, LF devices are not usually sensitive to radio interference. The
most popular LF RFID applications are access control and animal identification
(mainly for pets and livestock). They can be used for communications in harsh
environments (presence of metals and liquids) due to their long wavelengths.
However, they present low reading speeds and they are not recommended for
environments where many tags are together in a small space.

  * •

HF (High Frequency) RFID. Although the HF band goes from 3 MHz to 30 MHz, most
systems operate at 13.56 MHz. HF systems can reach a reading distance of up to
1 meter (they usually reach roughly 30 cm), what can lead to interference and
to the implementation of MAC (Medium-Access Control) mechanisms. This sort of
RFID systems is massively used in transportation, payment, ticketing, and
access control. HF systems present more interference than LF in unfavorable
environments, but they allow for a larger reader range, more speed, and more
space for information.

  * •

UHF (Ultra-High Frequency) RFID. The UHF band actually covers from 300 MHz to
3 GHz, but most systems operate in the Industrial, Scientific and Medical
(ISM) bands around 860-960 MHz and 2.45 GHz. UHF tags can be easily read at 10
meters, so they are ideal for inventory management and item tracking. It has
better performance in both transmission speed and distance with respect to LF
and HF systems. However, UHF tags consume more power, they are more sensitive
to harsh environments, and, generally, they store less information that HF
tags.

All these RFID systems can be also classified according to the way the tags
are powered:

  * •

Passive systems. They do not need internal batteries to operate, since they
rectify the energy sent through the reader’s antenna. There are passive LF,
HF, and UHF systems, which nowadays can be easily read at a 10-meter distance.

  * •

Active systems. They include batteries, what allow them to reach further
distances (usually up to 100 meters). Due to power regulations, almost all
active systems operate in the UHF band.

  * •

Semi-active, semi-passive or BAP (Battery-Assisted Passive) systems. They
decrease power consumption by using batteries just for powering the tags for
certain functionality. Commonly, batteries are used to power up the basic
electronics, while the energy obtained from the reader is used for powering
the communications interface. Tags have a battery to power part of the
circuitry, thus, they are more reliable than passive, but require more
maintenance.

As it was mentioned, RFID has been used previously for identifying and
tracking items in different industries. For instance, an example of a RFID-
enabled real-time manufacturing execution system is presented in RFID (2013).
There the authors describe devices that are deployed systematically to track
manufacturing objects and that collect real-time production data. The paper
also details a case study with a company that manufactures large-scale and
heavy-duty machinery, whose efficiency (planning and scheduling decisions) is
evaluated with real-life industrial data. Other applications of RFID include
tracking protective equipment smartEpis , appliances smartsocket , or maritime
freight containers freightcontainers .

NFC (Near-Field Communication) is a technology that evolves from RFID. NFC
devices can be passive (tags) or active (for example, smartphones). The
technology operates at 13.56 MHz, with a power that allows for the
communication between elements at a distance of less than 20 cm. Different
authors have studied the use of NFC for identification and positioning. A good
example is described in NFC (2015), where it is proposed a navigation system
for identifying and tracking tags indoors, where there are no GPS signals
available.

Bluetooth Low Energy (BLE), also known as Bluetooth Smart, is a Wireless
Personal Area Network (WPAN) technology oriented to short-range applications
(around 10 m) and small devices that is optimized for energy efficiency. It
works at 2.4 GHz, sharing the frequency band with other technologies like Wi-
Fi. Bluetooth is not designed for a particular application: it defines a
series of profiles representing a default solution for a particular use and
establishes the requirements for interoperation between devices. Each
Bluetooth device can support one or more of these profiles, being the most
common the ones that establish links between devices and send data between
them. Such devices include beacons (devices that broadcast certain information
periodically), which serve as a reference in an indoor location scenario. An
example of the use of beacons for tracking is detailed in BLE (2016). The
authors describe a real-time simulator using workers’ position data to support
manager’s decision making in a manufacturing system. In such a system, BLE
beacons are used to collect data easily in an experimental manufacturing line.

Similarly to Bluetooth, Wi-Fi (IEEE 802.11 a/b/g/n/ac) is also a widespread
and popular technology. It may work at 2.4 GHz or/and 5 GHz. Due to its
popularity, many researchers have studied its use for providing location and
tracking services. There are several techniques for indoor location over Wi-
Fi, which are based, in general, in determining the position of the clients
respect to the access points using the angle or the time of arrival of the
signals Maki et al. (2016), the RSSI (Received Signal Strength Indicator) Yang
(2015) or fingerprinting Fingerprinting (2016).

Another well-known technology is infrared communications. Infrared radiation
is a form of electromagnetic and thermal radiation of a frequency lower than
the visible light perceived by the human eye. Infrared sensors are opto-
electronic devices capable of measuring the radiation of the bodies found in
their field of view (it requires direct LOS (Line-of-Sight) between the reader
and the object). An example of a system that uses infrared for tracking is
described in Infrared (2016), where it is presented a sensing device that can
simultaneously monitor urban flash floods and traffic congestion.

Ultrasounds have also been used extensively for positioning and tracking. As
its name implies, ultrasounds are sound (mechanical) waves whose frequency is
above the threshold of human hearing. Ultrasonic sensors are devices capable
of converting sound signals to electrical signals. These devices operate like
radar or sonar, evaluating the echo produced by the waves to estimate the
distance between the reader and objects. A good overview of indoor ultrasonic
positioning systems can be found in Ultrasonic (2013).

One of the latest technologies that can be applied to identification is Ultra-
Wide Band (UWB). UWB is a short range radio technology that allows for the
transmission of large amounts of information over a wide spectrum of
frequencies, achieving a very low power density and very short duration
pulses. A detailed review of UWB indoor positioning systems and algorithms is
provided in UWB (2016).

ZigBee can be also used in positioning and tracking applications. ZigBee is a
technology for creating low-power and low-cost wireless sensor networks. It is
able create mesh networks of intermediate devices that allow for achieving
large coverage distances. All ZigBee devices are designed for low consumption
and high security (encryption). ZigBee transmission rate depends on the
operating frequency, which may differ among regions, varying between 20
kbits/s and 250 kbits/s. An example of the use of ZigBee in smart
manufacturing is provided in ZigBee (2011). There the authors described a
system that uses RFID devices as data collectors and a ZigBee wireless network
to serve as the communication system to transmit the data to the different
levels of the enterprise management.

DASH7 is a standard evolved from RFID that provides long range wireless
communications and which is designed for low power applications that require
low bandwidth (up to 200 kbits/s). It works at frequencies between 315 MHz and
915 MHz, and allows for the connection with objects on the move. In the
literature there are not many well-documented DASH7-based developments. An
exception is Dash7 (2015), where it is analyzed the DASH7 Alliance Protocol
v1.0 specification and two practical applications are detailed: bird tracking
and a greenhouse monitoring application.

ANT+ is a subsystem of the ANT base protocol (a technology designed for
wireless sensor networks, similar to BLE, but oriented to the use with
sensors) that defines a protocol stack that allows for the operation in the
2.4 GHz band. It is designed for interoperability and data transfer over a
network. Most ANT+ developments are related to fitness and healthcare. An
example is detailed in Ant+ (2013), where it is studied mobile health
monitoring systems in elderly patients. Such a paper proposes a proof of
concept solution that allows patients to measure their weight and blood
pressure with ANT+ sensors connected to their Android smartphones.

Z-Wave is a specification for wireless communications oriented to home
automation. Its aim is to minimize the consumption of the devices to make it
suitable to use batteries, reaching transmission speeds of up 100 kbits/s. It
works in the frequency rage around 900 MHz and it has a theoretical range (in
open space) of up to 100 m. Z-Wave creates a mesh infrastructure with at least
one controller and an end device. No academic sources that propose a Z-Wave
based system for identification or tracking were found.

WirelessHART is a wireless technology based on HART (Highway Addressable
Remote Transducer Protocol). HART is the implementation of a protocol of
industrial automation. This wireless technology works in the 2.4 GHz band,
creating networks through a mesh architecture capable of self-organization. An
example of the use of WirelessHART in a CPS system can be found in
WirelessHART (2015), where it is presented a system simulator to evaluate the
performance of wireless real-time mesh networks.

LoRa (Long-Range Wide Area Network) is a low consumption wireless network
protocol designed for secure and low cost communications in the field of
Internet of Things (IoT). It uses a frequency range just below 1 GHz, being
designed to communicate sensors in unfavorable environments. It uses a star
topology, where a device (gateway) forwards messages between end devices. The
connection with these devices in terms of frequency is negotiated according to
the distance and the message length, so as not to interfere with each other.
Thus, transmission speeds between 0.3 kbits/s and 50 kbits/s are achieved. A
comprehensive analysis of the LoRa performance is analyzed in LoRa (2016).

Another long-range technology is SigFox, which is actually a
telecommunications network that follows the style of cellular networks. It is
designed to provide low cost and low speed transmissions. Being a network
operated by a company, a subscription must be paid for its service that allows
up to 140 messages per device per day, 12 Bytes per message and a transmission
rate of 100 bits/s at 868 MHz. In LoRaandSigFox (2015) it can be found a good
reference for understanding the insights on the application of SigFox in
industrial applications.

Finally, RuBee (IEEE 1902.1) is a point-to-point wireless communication
standard based on magnetic waves. It works in low frequencies (131 KHz), what
implies that it has a long wavelength (around 2000 m) and a low transmission
speed (1200 baud). Its main feature is that it does not use radio waves, but
it emits magnetic waves, allowing for the communication in unfavorable
environments (with the constant presence of liquid, metal, and NLOS (Non-Line-
of-Sight) communications). By using such a low frequency, RuBee consumes
little power (a tag may last between 5 and 15 years) with a range of up to 15
m. An example of an end-to-end asset visibility model for military logistics
using RuBee is described in RuBee (2014).

##  3 Design of the System

###  3.1 Operational requirements of smart shipyard pipes

The military ICMCIS ; NATOReport ; TacticalWimax  and mission-critical
infrastructures Nets4cars ; AranduconSecurity ; AranduconSurvey  have unique
operational requirements. Security 1-Li2012 ; Fernandez2016 , safety,
robustness, interoperability challenges, as well as bureaucratic and cultural
barriers, stand in the way of the broad adoption of new CPS and IoT
applications fraga2016 . Based on the study of the real shipyard environment
described in the previous section, different research lines have been detected
to improve the efficiency in the pipe processing chain and have a significant
impact on the shipyard productivity. In this section, a set of operational
requirements grouped by functionality are assessed in order to cover the
scenario previously described.

####  3.1.1 Automating the identification of the pipes in the workshop

Nowadays, the identification of pipes is performed manually, which means that
operators have to spend part of their working time reading barcodes. This
process requires direct Line Of Sight (LOS) between the reader, and the tag
and it is susceptible to reading errors. Likewise, this approach is burdensome
and poses risks due to human errors (it is susceptible of not being performed
or being performed at incorrect time instants). Nevertheless, operators
require information on how to process pipes, thus they have to perform their
identification and read the information associated with each of them. The
system proposed should allow for carrying out the identification of the pipes
with the smallest possible error in order to avoid manual tasks from the
operators involved in the process. Additionally, the system should offer the
operators dynamic information about the work to be done on the pipe.

####  3.1.2 Location of the pipes

The present system can determine the location of a pipe in the workshop
visually or at the instants in which the quality control processes are
performed by middle management. However, the remaining time the pipe is in an
unknown position. This unawareness of the exact or approximate position of the
pipes causes loss of time due to the seeking for pipes. The system proposed
should locate in real or near-real time the pipes circulating in the workshop,
not only the ones being processed, but also those that are stored. Thus, the
system should trace and monitor in real-time the pipes within the workshop and
the ones that left the manufacturing area.

Note also that the location awareness of the pipes would help to automate
different tasks, like notifying an operator on the arrival of a pipe to
certain workshop stage.

####  3.1.3 Pipe tracking

The aim of pipe tracking is to find a system that identifies a pipe during the
production process.

Today’s operation faces many demands, the pipes can be on the workshop for
many years and they suffer from very aggressive processes (e.g. treatment with
acids) during its manufacturing. Pipes are made of metallic materials and they
are grouped on pallets in significant amounts (tens of units), which makes
difficult their visual and/or electronic identification.

####  3.1.4 Optimization of the manufacturing time

In the shipyard it has been observed that the pipes manufactured in the
workshop have different storage times: the oldest pipe may rust at the time of
assembly, while others (most of them) show no signs of external corrosion.
Knowing the real needs of demand for construction, or the available pipes and
the workshop capabilities, enhances the storage times to avoid problems of
excess of stock (i.e. space problems) and corrosion (e.g. rust). Thus, the
system should minimize part stock time, and consequently, it should decrease
the likelihood of exposure to external elements.

####  3.1.5 Route optimization

Once created a visualization system for locating the pipes, it will be
possible to improve the system’s capacity for providing additional
recommendations thanks to the identification information and the location data
collected. A good example is the optimization of routes for the transfer of
pipes. For instance, given a pipe placed in the cutting area, it would be
interesting to know what is the fastest route to move it to its storage spot
in the dock. The ultimate goal is to optimize manufacturing and assembly times
by obtaining the best routes for the transportation and final installation of
the pipes.

###  3.2 Technical requirements of smart shipyard pipes

####  3.2.1 Hardware requirements

This subsection reviews the main hardware needs focusing on the tagging system
and on the concerns regarding the deployment.

#### Tagging system

Electronic tags require a number of features to optimize their performance in
aggressive environments in terms of electro-magnetic propagation and exposure
to external interference (i.e. shocks, hits, pressure, acids, high temperature
liquids, among others). The following are the main constraints faced by an
electronic tag-based system when operating in the shipyard scenarios
previously described:

  * •

Deployment. Tags are deployed on a workshop, where there are different areas
(a detailed description of the workshop is given previously performed in
Section 1). In addition, it must be emphasized that tags should be as small as
possible so as not to cause problems during the treatment and handling of the
pipes.

  * •

Presence of metals. In the workshop there are many metallic elements that
originate signal reflections and interfere in RF communications in the HF and
higher bands. Therefore, only technologies prepared to tolerate the presence
of a significant level of metallic elements should be considered.

  * •

Presence of water. Navantia’s pipe workshop is not particularly cold, but it
is next to the sea, so humidity levels are relatively high (between 40% and
95%).

  * •

Exposure to liquids, acids, salinity, fuel or other corrosive substances. The
tags selected should support the degreasing, metal pickling, and rinsing
processes that are carried out in the workshop during the manufacturing of the
pipes. Specifically, Table 2 indicates examples of the exposure to different
chemical solutions, temperatures, and time durations that should be supported.

Table 2: Procedures for pipe cleaning.

Pipe type | Process | Solution | Temperature | Duration | Observation
---|---|---|---|---|---
Bent pipes | Degreasing | Water and T-149-E (6.25% $\pm$ 1) | 70°C$\pm$10°C | 15$\pm$5 min |
Pickling | Hot water | 60°C$\pm$10°C |  | PH: from 5 to 8
Carbon steel pipes | Degreasing | Water and T-149-E (6.25% $\pm$ 1) | 70°C$\pm$10°C | 45$\pm$5 min |
Pickling | Water and DECAPINOX C (10% $\pm$ 2) | 25°C$\pm$10°C | 15$\pm$5 min |
Rinsing | Hot water | 60°C$\pm$10°C |  | PH: from 5 to 8
Cooper alloys | Degreasing | Water and T-149-E (6.25% $\pm$ 1) | 70°C$\pm$10°C | 15$\pm$5 min |
Passive Pickling | Water and SCALE-GO (4,37% $\pm$ 1) | 50°C$\pm$10°C | 15$\pm$5 min |
Rinsing | Hot water | 60°C±10°C |  | PH: from 5 to 8
Stainless steel | Degreasing | Water and T-149-E (6.25% $\pm$ 1) | 70°C$\pm$10°C | 15$\pm$5 min |
Passive Pickling | Water and DECAPINOX C (10% $\pm$ 2). | 25°C$\pm$10°C | 15$\pm$5 min |
Rinsing | Hot water | 60°C$\pm$10°C |  | PH: from 5 to 8

Additionally, the following situations should be considered:

    * –

During cleaning, pipes might be exposed to pressurized water through a
pressure washing machine.

    * –

During testing, pipes could be also exposed to hot air, water and oil (in
hydraulic systems).

    * –

If the tags do not support the aggressive processes described in Table 2, it
has to be considered the addition of an external protection.

    * –

The encapsulation of tags/readers must be able to resists acids, salinity,
fuel and other substances that may corrode them.

  * •

Potential communications interference. The technology selected must be able to
transmit in the presence of the most common sources of electromagnetic
interference (e.g. Wi-Fi, Bluetooth, the use of mechanical saws) and other
unusual sources (e.g. radar tests, whose frequency ranges from tens of MHz to
GHz, and their power can reach several KW).

  * •

Reading distances. The monitoring system must be able to provide access to
location data from a remote computer. Such identification/location information
must be as accurate as possible, regardless of the distance required to read
the pipes/pallets to be monitored. It is important to note that the workshop
is 205 m long, so a network of readers would probably need to be created to
cover the whole building.

  * •

Tolerance to high temperatures. During manufacturing, pipes can be exposed to
high temperatures in two processes: while washing them in water/acids, or
during welding.

  * •

Pressure. Both during the storage and the transfer of the pipes it is possible
that they (and their tagging system) will be exposed to pressure due to the
accumulation of weight and collisions. Pressure varies depending on the weight
and strength supported by the base material. Note that, in general, between 30
and 35 pipes are moved into each pallet, and that such pallets support up to 2
tons.

The following are the most common situations where external pressures are
produced:

    * –

When moving a pallet with other pipes on top. There is no standard criteria
for stacking pipes, but, usually, the heaviest are placed at the bottom of the
pallet.

    * –

When lifting a block to its mounting position.

    * –

During manufacturing, it may be necessary to round heads/ends, thus strokes
can be applied, what involve deformations.

  * •

Battery duration. Since pipes arrive to the workshop, up to three years may go
by until they depart from the storage areas to be installed on a ship.
Therefore, battery should last at least such a period of time.

  * •

Mobility. The technological solution selected must provide portable readers
for dynamic and in-situ operation on the various identification, location, and
traceability systems.

#### Readers/scanners location

Readers should be located in places where there is access to both a data
network and electricity. Similarly, those locations should be in places where
they interfere the operator work as little as possible.

  * •

Electricity. The typical workshop usually has numerous electrical outlets in
different locations, thus it should not be a problem to power the hardware of
the system.

  * •

Data. Ethernet and Wi-Fi networks should be available to allow for receiving
and sending data to the readers deployed. In Navantia’s workshop, while these
networks are available in most of the workshop, the number of Ethernet
sockets, except in certain locations, is scarce, and they are almost always
associated to control equipment (e.g. the pipe storage robot or the bending
machines). For such a reason, it is almost essential to place switches or hubs
that allow for adding new Ethernet devices easily.

####  3.2.2 Software requirements

The system should include the following basic functionality regarding user
features:

  * •

The system should display the location of the pipes in the workshop in real or
near-real time. Ideally, the visualization should be implemented in a multi-
platform system, which should allow for monitoring the whole workshop from a
remote computer, a tablet, and even a smartphone.

  * •

Easy interaction with the basic pipe information. In addition to viewing pipes
in a map, it is desirable that users can access certain information about
them.

  * •

Filtering the pipes displayed. Once the system is operating, numerous pipes
would be displayed while moving through the workshop. Therefore, it is
convenient that a user can filter them in order to only show a specific pipe
or a subset that meet certain criteria.

  * •

The system should be able to issue different notifications about relevant
events that happen in the workshop. For instance, a Business Intelligence (BI)
module should issue a notification when a pipe goes from one workshop stage to
another (e.g. from the cutting area to bending).

###  3.3 Selection of the identification technology

####  3.3.1 Analysis of the technologies

After exposing the requirements, the technologies best adapted to the
application environment can be determined. A comparison that considers all the
factors mentioned such as deployment, presence of metals, presence of water,
exposure to liquids, acids, salinity, fuel or other corrosive substances,
potential communications interference, reading distances, tolerance to high
temperatures, pressure, battery duration, mobility, and cost, is shown in
Table 3.

Table 3: Comparison of the different identification technologies.

Factor | Deployment | Metal | Water | Corrosion | Interference | Reading dist. | Temperature | Pressure | Battery | Mobility | Cost
---|---|---|---|---|---|---|---|---|---|---|---
LF RFID |  |  |  | * |  |  | * | * |  |  |
HF RFID |  |  |  | * |  |  | * | * |  |  |
UHF RFID |  |  |  | * |  |  | * | * |  |  |
BLE |  |  |  | * |  |  | * | * |  |  |
Wi-Fi |  |  |  |  |  |  |  |  |  |  |
UWB |  |  |  |  |  |  |  |  |  |  |
Ultrasounds |  |  |  |  |  |  |  |  |  |  |
ZigBee |  |  |  |  |  |  |  |  |  |  |
DASH7 |  |  |  | * |  |  | * | * |  |  |
Z-Wave |  |  |  | * |  |  |  |  |  |  |
WirelessHART |  |  |  |  |  |  |  |  |  |  |
RuBee |  |  |  |  |  |  |  |  |  |  |

At the view of the table, it can be observed that some of the technologies
explained in Section 2.3 can be directly discarded, since they do not fulfill
any of the significant requirements of the system. The technologies that are
fully compliant with the operational and technical requirements described in
Sections 3.1 and 3.2 are in green color, the ones that partially fulfill the
requirements are shown in yellow, and the non-compliant ones are colored in
red.

Note that, in Table 3 an asterisk means that custom tags are required and that
they are currently on the market. For instance, high temperatures are only
supported by customized tags. The practical experience of Navantia has shown
that the upper peak temperature can be set at 105°C. This temperature can be
easily supported: regular electronic tags usually tolerate peaks of 125°C, but
specialized tags can reach up to 200°C or 300°C. The same happens with
corrosive chemicals (including water) or high pressure: the tags required have
to be designed to support them.

####  3.3.2 Discussion

This subsection is focused on describing and choosing the technology more
appropriate for automating the identification and location of the pipes in the
shipyard workshop. First, a number of technologies were discarded following
Table 3, since they do not properly address some of the most relevant
requirements of the system:

  * •

LF and HF RFID: the reading distance they reach is not enough for building a
ubiquitous real-time CPS system.

  * •

2.4 GHz RFID: due to its operating frequency, its performance decreases in the
presence of metals, liquids, or the interference from other systems that work
on the same frequency band.

  * •

BLE and Wi-Fi: these technologies share operating frequency with the 2.4 GHz
RFID, so they suffer from the same problems in terms of interference.

  * •

ZigBee, Z-Wave, and WirelessHART. These technologies are aimed at creating
sensor networks: their application in location is possible, but they are not
suitable for the shipyard environment described in this paper. In addition,
ZigBee and WirelessHART work in the 2.4 GHz band, which must be avoided.

Next, it is possible to discard technologies that, by their nature, they
cannot be rejected at a first instance, but which pose risks:

  * •

Ultrasounds. Its biggest disadvantage is the need for direct LOS between the
reader and the tags. Although the frequencies used do not interfere directly
with the test environment, they can interfere with sensor communications
and/or armament of the ships, and even with marine animals.

  * •

UWB. They obtain an excellent precision in location applications, but it is
difficult to adapt to the shipyard environment described due to its short
range and its problems with the presence of metal objects.

Finally, three technologies were chosen for their theoretical characteristics,
and because they are suitable for the shipyard environment. These are:

  * •

RuBee. It does not suffer from electromagnetic interference. Moreover, RuBee
tags are designed to withstand adverse conditions and their battery lasts up
to 15 years. It has been also tested in weapon control environments, where it
was shown that its use is safe Pantex .

  * •

DASH7/active UHF RFID. Both UHF RFID and DASH7 work in a frequency range to
some extent sensitive to the interference present in the shipyard environment,
but that it is slightly less aggressive than in the 2.4 GHz band. They have a
theoretical reading distance of up to 100 m.

  * •

UHF RFID. As it has been already mentioned, the use of frequencies below 1 GHz
decreases the influence of the environment conditions. UHF technology has the
advantage of having been tested thoroughly in location and tracking
applications. In addition, tags are usually inexpensive.

###  3.4 Communications Architecture

The communications architecture proposed relies on an infrastructure of
beacons that identify pipes throughout different areas. Such an architecture
is illustrated in Figure 5.

Each of the beacons reads the identifiers of the pipes/pallets and estimates
its position. Contrary to the current Navantia system, it is possible to put
aside the concept of pallet, allowing the system to focus only on pipes. The
removal of the concept of the pallet simplifies the deployment. Tags are only
on the pipes, concentrating the logic of identification, location and tracking
in the readers that are deployed. Therefore, a careful planning of the
location infrastructure is required. It is possible to increase the accuracy
of the system by scaling it, increasing its granularity (i.e. the number of
readers deployed), in exchange for an increase in the economic cost.

Figure 5: Communications architecture of the smart pipe system.

##  4 Implementation

In the previous Section it was concluded that there were three technologies
with high potential to carry out the identification and location of the pipes:
RuBee (IEEE 1902.1), DASH7 (active UHF RFID) and passive UHF RFID. After
obtaining these conclusions, various suppliers of each of the technologies
were contacted. In the case of RuBee an unexpected restriction arose: the only
worldwide supplier refused to sell the hardware (as a distributor would do),
because recently they had changed their business model (as of writing, they
only sell completely closed projects, from design to implementation). Thus,
the range of technologies was reduced to active and passive RFID.

Figure 6: Modules of the smart pipe system proposed.

###  4.1 System modules

The system was designed assuming an RFID-based implementation and it consists
of the software modules shown in Figure 6. Such modules perform the following
tasks:

  * •

Location module: it is the core of the system. It obtains the coordinates of
each tag after processing the signal strength.

  * •

RSS acquisition module: it is the interface with the RFID readers. It allows
for obtaining the signal strength from each tag.

  * •

RSS collection module: it is in charge of storing the signal strength values
in the RSS database.

  * •

Business Intelligence module: it decides which notifications should be
launched depending on the current position of a pipe, its historical position
(where it has been in the past), and the states through which it has passed.

  * •

Display module: it displays the positions of the pipes and the relevant
notifications on a user screen. It allows operators to filter the pipes based
on various parameters for easy viewing.

  * •

SAP/ Manufacturing Execution System (MES) client: it allows for obtaining the
data about the characteristics of the pipes, which are stored in different
remote repositories.

The processing and collection modules were implemented in the programming
language Python. Django was used as a web framework and Nginx as a web server.
The display module was implemented using websockets, Javascript, and jQuery.

There are also three databases that collect the information needed by the
system (in the implementation presented in this paper, they all are SQLite
databases):

  * •

RSS database: it stores the signal strength values collected by the RSS
Collection Module. At the same time, it can be used by the real-time Location
Module to determine the location of the tag.

  * •

Pipe information database: it stores the information received about the pipes
from third-party systems such as SAP or MES.

  * •

Business intelligence rules database: it contains the necessary rules for the
BI system to determine when to launch notifications.

###  4.2 RSS-based Location Techniques

This section describes the models and techniques used in the implementation of
the Location module. Its experimental behavior will be analyzed in the tests
performed in Section 5. The algorithms include modeling RSS respect to the
distance, a Kalman filter to reduce the noise, or spatial diversity techniques
to increase RSS stability. Note that, in this paper, the term RSS is used
instead of Received Signal Strength Indicator (RSSI): RSS is more generic than
RSSI, which is commonly used for estimating signal strength in Bluetooth and
Wi-Fi devices.

####  4.2.1 RSS Mathematical Model

RSS can be used to determine the location of each tag by relating it directly
to the signal propagation. However, this method depends on the variability of
RSS, which is influenced by obstacles and by the presence of metallic
elements. In fact, indoors, the propagation of radio waves is mainly affected
by two types of losses: the path loss and the losses due to small and large
scale fading. Fading is usually associated with reflections, diffraction or
absorption, commonly present in real environments. The small scale fading
arises due to the multipath propagation effect, while large scale fading is
related to the shadowing effect. From the RSS values it is possible to derive
the mathematical model that allows us to relate them with the distance. The
simplest approach is to average the RSS values for each distance. Another
approach is based on the construction of a mathematical formula that model the
system behavior. The proposed formula which closely reflects the path loss in
the indoor environment is the log distance path loss model. This model can be
seen as a generalization of both the free space propagation model and the two-
ray ground propagation model. Thus, the behavior of the signal can be
simplified through the following model Rappaport2002 :

$\centering PL(d)[dB]=PL(d_{0})+10\,n\,log(d)+X_{\sigma}\@add@centering$ (1)

where:

  * •

$PL(d)[dB]$ is the attenuation in decibels suffered at distance $d$.

  * •

$PL(d_{0})[dB]$ is the attenuation in decibels at a reference distance
$d_{0}$. Typically obtained through measurements.

  * •

n is a calculated value that minimizes the Minimum-Squared Error (MSE)
difference between the model and the empirical results.

  * •

$X_{\sigma}$ is a Gaussian variable with mean zero and standard deviation
$\sigma$.

Considering the model and using the RSS as inputs, the distance $d$ can be
easily obtained with the expression:

$\centering d=10^{\frac{RSS-RSS_{d_{0}}}{10*n}}\@add@centering$ (2)

####  4.2.2 Kalman filtering

The fundamental problem that faces the RSS indoors is the noise, which causes
inaccuracies when determining the location. There are different methods to
filter noise, among which it was chosen one that is popularly used for
guidance, navigation and control of vehicles (mainly spaceships): the Kalman
filter. A detailed explanation of how Kalman filtering works is out of the
scope of this paper, but the interested reader can check Grewal2008  for a
detailed description of the theory behind it. For the experiments shown in
Section 5, Kalman filtering was adapted to reduce noise on RSS, based on the
work described in Bulten et al. (2016).

First, it should be clarified that a regular Kalman filter assumes that its
model is linear (there is an extended version for non-linear models), so the
transition from the current state to the next state should be performed
through a linear transformation. Hence, it can be formulated first the general
Kalman filter expression for a transition:

$\centering x_{t}=A_{t}x_{t-1}+B_{t}u_{t}+\epsilon_{t}\@add@centering$ (3)

where $x_{t}$ and ($x_{t-1}$) are the current and previous states,
respectively, $A$ is a transformation matrix, $u_{t}$ is a control input, $B$
is the control input model, and $\epsilon$ is the noise.

This general expression can be simplified by assuming that pipes remain in
specific places during the measurements performed. Thus, RSS is expected to be
constant, having a varying contribution from the noise. Then, the control
input $u_{t}$ can be ignored and it can be assumed $A_{t}$ to be the identity
matrix, what results in the following expression:

$\centering x_{t}=x_{t-1}+\epsilon_{t}\@add@centering$ (4)

Regarding the observation model, in general, it is expressed as:

$\centering z_{t}=C_{t}x_{t}+\delta_{t}\@add@centering$ (5)

where C is a transformation matrix and $\delta$ the noise related to faulty
measurements. Since RSS observations and states are equal (RSS is estimated by
using old RSS values), the transformation matrix C becomes the identity
matrix, so the expression ends up being as follows:

$\centering z_{t}=x_{t}+\delta_{t}\@add@centering$ (6)

Once transitions have been defined, the prediction step can be formulated.
Such a step indicates what is expected for the next state without taking
measurements into account. Since RSS is expected to be constant, the
expression is simple:

$\centering\overline{\mu_{t}}=\mu_{t-1}\@add@centering$ (7)

$\centering\overline{\Sigma_{t}}=\Sigma_{t-1}+R_{t}\@add@centering$ (8)

In contrast to $x$, which is the true value, $u$ is the value predicted.
$\Sigma$ is the certainty of the prediction. Such a certainty depends on the
previous certainty and on the noise $R$. In addition, the bar over
$\overline{u_{t}}$ and $\overline{\Sigma_{t}}$ means that it is still needed
to incorporate the information added by the measurement.

With the prediction estimate $\Sigma$ it is possible to calculate the Kalman
gain, which is defined as:

$\centering K_{t}=\overline{\Sigma_{t}}(\Sigma_{t}Q_{t})^{-1}\@add@centering$
(9)

where $Q$ is the measurement noise. Then, the update step can be obtained,
where it is calculated the prediction $\mu$ and the certainty $\Sigma$:

$\centering\mu_{t}=\overline{\mu_{t}}+K_{t}(z_{t}-\mu_{t})\@add@centering$
(10)

$\centering\Sigma_{t}=\overline{\Sigma_{t}}-K_{t}\Sigma_{t}\@add@centering$
(11)

From this equation it can be observed that, the larger the Kalman gain, the
bigger the influence of the measurement on the estimation. In the same way, if
the Kalman gain is low, it is trusted more the prediction than the
measurement.

####  4.2.3 Spatial diversity techniques

Multiple-input Multiple-output (MIMO) technology offers substantial
performance gains in wireless links MIMO (2013). Thanks to the use of multiple
antennas for transmission and/or reception, the serious effects produced by
fading on the RSS can be decreased. Likewise, it is possible to use spatial
diversity to improve the stability of the mathematical model proposed thanks
to the fact that there are several receiving antennas. To reduce the
impairments caused in the signal by these environments, spatial diversity
takes advantage of the fact that there exists a low probability of getting
simultaneously a deep fading on all the signal paths. Then, assuming
uncorrelated channels and using spatial diversity techniques, it is possible
to combine the RSSs in such a way that the effects of fading and multipath can
be reduced.

These techniques are based on the classic algorithms of combination and
selection, typically used in systems that try to increase the capacity or to
stabilize the signals received. Although in this article the same names as the
ones used in the traditional algorithms are used, this is not totally precise,
because the implementations made are adapted to the RSS parameter Fernandezet
al. (2007), modifying the principles of the classic schemes, which are focused
on data processing.

#### Combination Methods

  * •

Equal Gain Combiner (EGC). This method weights equally all the antennas,
performing the average of all the RSSs, following the equation:

$\centering EGC=\frac{1}{N}\sum_{i=1}^{N}RSS_{i}\@add@centering$ (12)

where $N$ is the number of antennas and $RSS_{i}$ is the level of received
signal for the $i$-th antenna. For the sake of clarity, in the curves shown in
the experiments section, the term "Mean" was used instead of "EGC", but its
computation is identical.

  * •

Maximum-Ratio Combiner (MRC). Unlike EGC, this method weights each RSS
depending on its signal quality. That is, it gives more weight to the most
positive RSSs than to the lower ones. To carry out this computation the
following expression was chosen:

$\centering
MRC=\sum_{i=1}^{N}\left\\{\frac{RSS_{i}-RSS_{min}}{\sum_{i=1}^{N}\left\\{RSS_{j}-RSS_{min}\right\\}}\right\\}RSS_{i}\@add@centering$
(13)

where $RSS_{min}$ is the smallest RSS that can be obtained.

#### Selection methods

  * •

Selection Combiner (SC). This technique consists in sorting the available
measurements from higher to lower, choosing only one value. The aim of this
method is to achieve the best RSS level for each instant, without considering
the RSS fluctuations. In practical applications, the highest value is usually
chosen, which is the criterion used in the implementation evaluated in the
Experiments Section.

  * •

Switch-and-Stay Combiner (SSC). This algorithm first chooses one antenna,
through which the RSS is received until it falls under a threshold, which has
been previously set. In that moment, the algorithm switches to the next
antenna without verifying its RSS. There exists the possibility of changing
the threshold dynamically, but in the present paper it is only considered the
case when the threshold remains constant. It is important to emphasize that
the switching between antennas is performed in a blind way, so it is not
guaranteed to get a better RSS with the switching. Without a doubt, the most
important issue for this method is the calibration of the optimum switching
threshold. To carry out this task, the optimum threshold was decided according
to an estimator of the signal dispersion. Specifically, in the experiments,
the threshold was set depending on the signal variance.

  * •

Scanner Combining (ScanC). This method is similar to SCC, but instead of
making a blind switching when the threshold is exceeded, it checks each
antenna until it finds one over the threshold. If there are not any antennas
above the threshold, the method keeps on using the same antenna. Like with
SCC, the value of the switching threshold is very important, because it
decides the system behavior. Besides, as in the SCC algorithm, during the
experiments, it was considered a switching threshold that minimizes the
variance.

##  5 Experiments

This Section presents the results of several tests conducted to validate the
technologies selected and the CPS software developed. Regarding the tests,
they were performed to determine which of the two technologies selected,
passive and active RFID, adapted better to the peculiarities of the
environment and the characteristics of pipe workshop.

###  5.1 Selected Hardware

Based on the requirements enumerated in Sections 3.1 and 3.2, the most
promising readers and tags for both technologies were selected. They are
described in the next subsections.

####  5.1.1 Passive RFID hardware

The passive UHF reader selected was a Speedway Revolution R420 from Impinj
Speedway . The reader has connections for up to 4 antennas (four panel high-
gain antennas were used during the tests) and the ability to exchange data via
Ethernet, USB, RS-232, or a GPIO port. Reader data is accessed through its
native REpresentational State Transfer (REST) Application Programming
Interface (API). Similarly, a mobile reader (A6-UHF Long Range) based on
Windows CE was chosen to provide mobile identification to operators
A6-UHFLongRange .

A wide range of tags allows for carrying out a reliable validation of the
technology thanks to its diverse nature/objectives. Specifically, the
following models from Omni-ID Omni-ID  were selected: Fit 400 UHF Tag on-
metal, Exo UHF Tag on-metal family (Exo 600, Exo 750, and Exo 800), Dura UHF
Tag family (Dura 600, Dura 1500, and Dura 3000), and Adept 360°-ID UHF Tag on-
metal. Its main physical specifications can be seen in Table 4.

Table 4: Specifications of the passive RFID tags selected.

Family | Model | Max. Reading Range | Dimensions | Weight
---|---|---|---|---
Fit | 400 | 4 m | 13.1$\times$7.1$\times$3.1 mm | 380 g
Exo | 600 | Fixed reader: 6 m, handheld: 3 m | With holes: 80$\times$15$\times$12 mm, without: 60$\times$15$\times$ 12 mm | 12 g
750 | Fixed reader: 7 m, handheld: 3.5 m | 51$\times$48$\times$12.5 mm | 25.6 g
800 | Fixed reader: 8 m, handheld: 4 m | 110$\times$25$\times$13 mm | 26 g
Dura | 1500 | Fixed reader: 15 m, handheld: 7.5 m | 140$\times$66$\times$14 mm | 75 g
3000 | Fixed reader: 35 m, handheld: 20 m | 210$\times$110$\times$21 mm | 265 g
600 | Fixed reader: 5 m, handheld: 2.5 m | 49$\times$38$\times$9.5 mm | 12 g
Adept | 360 | 10 m | 136.5$\times$48$\times$5.5 mm | 126 g

The Fit 400 is a small form factor, high performance RFID tag, it is the
perfect solution when space is limited, but performance is demanded. With
respect to the Exo family, Omni-ID Exo 600, with a small footprint, is well
suited for being attached to metal bars. Omni-ID Exo 750 is designed with a
broad read angle and with a global RF response. Omni-ID Exo 800 is a long
range passive UHF RFID tag capable of being read on, off, and near metal
surfaces. Designed in a surprisingly small form factor, it features a rugged
design for long term use outdoors and in industrial environments.

In the Omni-ID Dura family, Dura 600 is a small form factor RFID tag, with
extreme impact resistance, and superior on-metal performance. Omni-ID Dura
1500 is a durable and long range tag. Designed with heavy industry in mind, it
features extreme impact resistance and high temperature ratings. Omni-ID Dura
3000 is designed for heavy industry and outdoor applications. Its features
include high impact resistance, water proof, and a durable case (it is
optimized for tracking large assets in open storage environments, without
worrying about batteries).

The Omni-ID Adept 360°is an UHF RFID tag for the harshest environmental
applications. The tag is encased in an industrial steel frame with a tether
attachment designed to meet the needs of heavy industry applications.

####  5.1.2 Active RFID hardware

The active reader chosen was NPR ActiveTrack-2 NPR , that, according to the
manufacturer, has a coverage radius of 45 meters with standard antennas. High
gain antennas were acquired to extend its coverage to about 90 meters.

Different tags can be used with the chosen reader (i.e. different sizes,
different features). Among all the models, the Active RuggedTag-175S Active
RuggedTag-175S  was chosen, since it is designed to stand aggressive
environments and is sonically welded (what helps to resist the effects of
maritime environments). According to the manufacturer, its lithium CR2032
battery lasts more than 4 years. Its dimensions are
63.75$\times$37.72$\times$25.4 mm and a weight of 51 g.

###  5.2 Test methodology

The tests were conducted with the readers inside Navantia’s pipe workshop.
These tests were focused primarily on assessing the most favorable cases for
determining how far RFID tags can be read in the best case scenario: if the
results for the best case are not as good as expected, then, obviously, the
system will perform worse in more complex situations.

Two different kinds of experiments were performed. First, it was obtained the
maximum reading distance, taking diverse factors into account (e.g. the type
of tag, the number of reading antennas, type of antennas, or the shape of the
antenna array). The second kind of experiments were associated with obtaining
a mathematical function that relates signal strength with distance in order to
locate accurately the tags.

Figure 7: Measurements with passive UHF reader with two antennas.

Figure 7: An example of tags used for measurements.

(a) Exo 750 UHF Tag.

(b) Dura 1500 UHF Tag.

(c) Adept 360 UHF Tag.

###  5.3 Passive RFID tests

####  5.3.1 Maximum reading distance

These tests were conducted to determine, for each of the tags acquired, the
maximum distance at which the tags can be read when they are oriented in the
most favorable position (parallel to the reader antennas). Specifically, these
tests were carried out in a side of the pipe workshop, taking advantage of its
width (about 17 meters). At one end of the workshop, the passive UHF reader
was placed with their antennas and a pipe of 31 mm diameter with the different
tags adhered was placed in a wheel cart.

The layout of the different elements used in the measurements can be seen in
Figures 7 and 7(c). In these first measurements, which were focused on the
determination of the maximum reading distance, only two antennas were used.
When tests were performed to include spatial diversity, four antennas were
used (with more antennas distributed over the reading area, it is more likely
to capture the signal or some reflections). Thus, on the left of Figure 7 it
is shown the system while capturing with an array of four antennas.

Figure 7: Measurements with passive UHF reader with four antennas.

Table 5 shows a summary of the reading distances achieved when reading the
passive tags selected at different distances through the two panel antennas.
The distances at which a good percentage of the readings is achieved (i.e.
when readings are obtained more than 95% of the time) are colored in green.
The distances where no readings were obtained, or where a reading was achieved
sporadically, are in red. Table 5 allows us to conclude that Exo 800, Dura
1500, Dura 3000 and Adept 360°are the tags that can be read further. However,
Dura 3000, and Adept 360°have to be excluded from the final selection for this
specific application: the former is so big (210 mm x 110 mm x 21 mm) that it
would hinder the operator work on the pipe, while the latter has an inadequate
form factor (it is designed to be hanged).

In the rest of the paper, for the sake of brevity, most of the curves
represented will refer only to Exo 800 tags, since the conclusions draw from
the experiments are identical to the ones obtained with the Dura 1500.

Table 5: Reading distances achieved with the different tags.

Tag Reading Distance | 1 m | 2 m | 3 m | 4 m | 5 m | 6 m | 7 m | 8 m | 9 m | 10 m | 11 m | 12 m | 13 m | 14 m | 15 m | 16 m | 17 m
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---
Fit 400 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
Exo 600 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
Exo 750 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
Exo 800 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
Dura 1500 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
Dura 3000 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
Dura 600 |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |
Adept 360° |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |  |

####  5.3.2 Improving the antenna’s reading angle

The beam of the panel antennas of the UHF system is relatively narrow on
purpose, in order to increase reading distance. However, in exchange, the
maximum reading angle in which a tag can be read is reduced. To quantify such
an angle, two antenna configurations were tested: a linear array (on the left
in Figure 7) and an ’L-shaped’ array (in Figure 7, on the right). The study of
the system coverage was performed for the two tags that offered a better
balance between range and physical size (i.e. Exo 800 and Dura 1500).

Table 6(a) shows the reading range for the Dura 1500 when using the linear
array. Table 6(b) shows the results of the experiment for the Exo 800 tag. In
the horizontal axis of the table it is indicated the tag movement respect to
the array of antennas, being 90°the existing angle when facing the four
antennas. The conclusions drawn are identical for both: it can be clearly seen
that, when moving the tag to obtuse angles (between 90°and 180°), reading
distance decreases. Some improvement in reading may be achieved at certain
distances, but this is due to punctual reflections that occur in the specific
location where the measurements were performed. Therefore, the table indicates
that a configuration of antennas aligned obtains a good maximum distance as
long as tags are located preferably frontwards, but, as the tag moves toward
more obtuse angles (and similarly to more acute angles), the system loses much
of its reading range. This means that the system would also work in places to
control the movement of the tags through certain areas (for example, when
moving from one room to another), but it would not work for a constant
monitoring of all the workshop tags.

The results for the ’L-shape’ array are shown in Table 6(c): they show that,
in exchange for losing reading distance (because two frontal antennas are used
instead of four), the probability of the reading one side of the array is
improved significantly.

Table 6: Distance/angle respect to the different antennas configuration.

Distance/angle | 180° | 160° | 135° | 130° | 110° | 90°
---|---|---|---|---|---|---
0.5 m |  |  |  |  |  |
1 m |  |  |  |  |  |
2 m |  |  |  |  |  |
3 m |  |  |  |  |  |
4 m |  |  |  |  |  |
5 m |  |  |  |  |  |
6 m |  |  |  |  |  |
7 m |  |  |  |  |  |
8 m |  |  |  |  |  |
9 m |  |  |  |  |  |
10 m |  |  |  |  |  |
11 m |  |  |  |  |  |
12 m |  |  |  |  |  |
13 m |  |  |  |  |  |
14 m |  |  |  |  |  |
15 m |  |  |  |  |  |
(a) Dura 1500 tag with linear array.

Distance/angle | 180° | 160° | 135° | 130° | 110° | 90°
---|---|---|---|---|---|---
0.5 m |  |  |  |  |  |
1 m |  |  |  |  |  |
2 m |  |  |  |  |  |
3 m |  |  |  |  |  |
4 m |  |  |  |  |  |
5 m |  |  |  |  |  |
6 m |  |  |  |  |  |
7 m |  |  |  |  |  |
8 m |  |  |  |  |  |
9 m |  |  |  |  |  |
10 m |  |  |  |  |  |
11 m |  |  |  |  |  |
12 m |  |  |  |  |  |
13 m |  |  |  |  |  |
14 m |  |  |  |  |  |
15 m |  |  |  |  |  |
(b) Exo 800 tag with linear array.

Distance/angle | 180° | 160° | 135° | 130° | 110° | 90°
---|---|---|---|---|---|---
0.5 m |  |  |  |  |  |
1 m |  |  |  |  |  |
2 m |  |  |  |  |  |
3 m |  |  |  |  |  |
4 m |  |  |  |  |  |
5 m |  |  |  |  |  |
6 m |  |  |  |  |  |
7 m |  |  |  |  |  |
8 m |  |  |  |  |  |
9 m |  |  |  |  |  |
10 m |  |  |  |  |  |
11 m |  |  |  |  |  |
12 m |  |  |  |  |  |
13 m |  |  |  |  |  |
14 m |  |  |  |  |  |
15 m |  |  |  |  |  |
(c) Exo 800 tag with ’L-shaped’ array.

####  5.3.3 Modeling RSS vs distance

As it was concluded previously, the two most promising tags are the Dura 1500
and the Exo 800 tags. It was evaluated their performance in a LOS scenario.
Figure 8 shows the distribution of RSS (measured for this Figure and for the
others presented in this paper in dBm) in relation to the distance when
evaluating the Exo 800: it can be seen how the signal levels decline as the
distance with the reader increases. From these RSS values it is possible to
look for the mathematical model that allows us to relate them with the
distance between the reader and the tag. Therefore, in order to determine the
location of a pipe in the workshop, it has to be found a mathematical function
that takes as an input the received signal level of a tag and that returns, as
an output, the estimated distance to the reader.

Figure 8: Exo 800: RSS for each antenna. Figure 9: Exo 800: mean curves for
each antenna and model obtained with the mean of the four antennas.

As it was explained in Section 4.2, there are different ways to obtain this
mathematical function. The most direct way is to average the RSS for each
distance, what is represented in Figure 9. In the same Figure, the theoretical
model is also adapted to the RSS received and plotted for $PL(d_{0})$ and $n$
values equal to -54.5 dBm and 1.8638, respectively. Ideally, the RSS-based
curves should follow a model to obtain a mathematical function that relates
RSS to distance, but it can be observed that, in practice, RSS oscillates
through distance.

Additionally, it is relevant to highlight that there are antennas at the sides
of the linear array that receive no signal at different distances (for
instance, in this LOS scenario, antenna 2 is the only one that receives RSSs
at a distance of 14 m and 15 m).

####  5.3.4 Reducing noise: Kalman filtering

Theoretically, RSS values only should depend on the distance between a tag and
the reader. However, in reality, signal strength is influenced by the
environment (for instance, the reflections created by metal objects or the
absorption related to the presence of water in the air) and, consequently,
signals include high levels of noise. In order to filter it, a Kalman filter
was applied as described in Section 4.2.2.

Figure 10 compares the results shown in Figure 9 with the ones obtained after
applying Kalman filtering to each antenna. It can be seen how the filtered
versions of the curves are, in general, slightly close to the theoretical
model and seem to be smoother (i.e. more stable). However, at the sight of
Figure 10, it is not obvious the improvement added by the filter (it can be
perceived better in the next subsection, where multi-antenna techniques are
also applied).

Figure 10: Exo 800: Comparison of the RSS curves with and without Kalman
filtering.

####  5.3.5 Stabilizing RSS with spatial diversity techniques

The techniques detailed in Section 4.2.3 can be applied easily to the RSS
signals collected by the four antennas of the passive UHF RFID system. Figures
11 to 14 show the results obtained after applying MRC, SC, SSC and ScanC
techniques. Note that, since there are no RSSs for some antennas at certain
distances, the curves end before reaching 15 m. The Figures show that,
although the multi-antenna techniques yield curves relatively smooth for low
distances, they oscillate for the highest distance values. At a plain sight,
it can be observed that the curves for ScanC are the ones that oscillate the
most, while the application of MRC, SC and SSC yields curves with an almost
constant slope.

Regarding the results obtained for the techniques after applying the Kalman
filter designed, it can be stated that the resulting RSS curves are really
similar. To quantify the stability of the RSS obtained and the level of
improvement achieved thanks to filtering, the Euclidean distance of the curves
obtained respect to the curve of the theoretical model was measured. The
results are shown in Figure 15. It can be concluded that the addition of
antennas stabilizes the RSS and creates curves more similar to the model.
Moreover, the use of Kalman filtering, as of implemented, enhances the
stability of the multi-antenna techniques applied, but in some cases, the
gaining is small.

Table 7 shows the mean error of every technique when estimating the distance
based on the RSS received for the Exo 800 tags. The table quantifies the
improvement achieved by using Kalman filtering: except for the two-antenna
MRC, the rest of techniques clearly improve their precision. Furthermore, it
can be observed that the SC technique is the most accurate, while ScanC is the
one that obtains the worst results, with a precision of roughly 2 meters.

Table 7: Mean error (in meters) of the different multi-antenna techniques. #Antennas Technique | MRC | Filt. MRC | SC | Filt. SC | SSC | Filt. SSC | ScanC | Filt. ScanC
---|---|---|---|---|---|---|---|---
2 | 1.6006 | 1.6235 | 1.0791 | 1.0738 | 1.5583 | 1.0882 | 2.3467 | 2.3399
3 | 1.3210 | 1.2236 | 0.9034 | 0.7967 | 1.1668 | 0.7398 | 2.1751 | 2.1790
4 | 1.0769 | 1.0061 | 0.6759 | 0.5802 | 0.9381 | 0.7398 | 2.0629 | 2.0119
Figure 11: Stabilizing Exo 800 RSS with the MRC technique. Figure 12:
Stabilizing Exo 800 tag RSS with the SC technique. Figure 13: Stabilizing Exo
800 tag RSS with the SSC technique. Figure 14: Stabilizing Exo 800 tag RSS
with the ScanC technique. Figure 15: Comparison of RSS stabilization
techniques applied to the Exo 800.

####  5.3.6 Key findings

After analyzing the results obtained indoors with LOS, it seems that the
passive UHF RFID system is suitable only for situations where transitions
between areas want to be controlled: due to its limited reading range, it is
not useful for a ubiquitous real-time control. Moreover, such a limited range
also increases hardware costs, since the larger the area to cover, the more
readers and antennas would be needed for the deployment.

An ’L-shaped’ antenna array helps to mitigate the reduced reading angle of the
system, but it decreases to almost a half the reading distance. Regarding the
stabilization of the RSS, the results obtained show that it is possible to
reduce the noise (and, therefore, increase the accuracy) of the system by
exploiting spatial diversity and applying Kalman filtering.

###  5.4 Active RFID

Tests were conducted with the active reader following the same methodology as
in the passive case: first it was measured the propagation with LOS, and then
the tags were tested at different angles and locations throughout the
workshop.

####  5.4.1 Maximum reading distance

Before performing the tests in the same way as with the passive system, the
total reading range of the tag was measured: it was found that the active tag
could be read 95% of the time at a distance of 100 m when using high-gain
antennas. This figure is greater than the one indicated by the manufacturer in
a LOS scenario (90 m), but it must be taken into account that in the pipe
workshop there are many metal objects (for instance, the ceiling is metallic),
what generates reflections that, in this case, favor the propagation of
wireless signals.

Next, the reading distance was measured like it was previously done for the
passive system in order to carry out a fair comparison between both RFID
systems (one of the moments during the measurement campaign is shown in Figure
16). Thus, in this process, the reader remained static at a point, while a
pipe placed in a wheeled cart had an Active RuggedTag-175S attached.

Figure 16: Measurements with the active UHF reader.

Note that the reader can use omnidirectional and high-gain antennas, but since
the latter provide more reading range, the results obtained when using them
will be the only ones shown in this Section.

####  5.4.2 Modeling RSS vs distance

Figure 17 shows the RSS values obtained for the active RFID system. Like in
the passive system, in such a Figure it can be appreciated that signal levels
decrease as the distance between the reader and the tag increases. It is
relevant to note that between 10 and 14 meters a light stabilization occurs,
which is associated almost certainly to the existence of reflections from
metallic elements (at that point it is where the robotic storage of small
pipes is placed).

The model explained in 4.2.1 can be applied (with $PL(d_{0})$ equal to -56.5
dBm and $n$ equal to 1.8261) to the RSS collected from the active reader. The
curve for the model is shown in Figure 18, where it can be observed that, for
low distance values, the mean RSS from both antennas follows the model
closely, but as the distance increases, RSSs oscillate remarkably.

Figure 17: RSS values when using high-gain antennas. Figure 18: RSS means and
multi-antenna techniques when using high-gain antennas.

####  5.4.3 Kalman filtering

A Kalman filter can be apply to the mean RSS of each individual antenna and
for the mean of both. The resulting curves are shown in Figure 19. Like in the
passive case, it seems that the filtered RSSs generate slightly more stable
curves. Nonetheless, the improvement added by the filter is observed better
through the results obtained for the multi-antenna experiments described in
the next subsection.

Figure 19: Comparison of the RSS curves when using Kalman filtering in the
active system.

####  5.4.4 Increasing RSS stability through spatial diversity techniques

It is possible to take advantage of the spatial diversity offered by the two
reader antennas to try to improve the stability of the RSS curves. The results
obtained for the MRC, SC, SSC, and ScanC techniques are also shown in Figure
18. Likewise, these results can be compared among them respect to the
theoretical model, and when applying Kalman filtering: as it can be concluded
from Figure 20, MRC and SSC obtain similar results, and, in all cases Kalman
filtering improves the stability of the RSS.

Table 8 shows the mean error for every multi-antenna technique when estimating
the distance based on the RSS received. In the table it is computed the mean
error for different reading distances in order to verify what can be observed
after analyzing the RSS curves visually: for distances of up 10 m the RSS
follows closely the theoretical model, but after that distance, RSS
oscillations alter the estimations, deriving into a clear loss of accuracy.
Thus, up to 10 m the precision of system is around 1 m, but it is increased to
3-4 m for higher distances. The table also shows the worsening of the accuracy
when increasing the reading distance from 16 m to 17 m: although there is only
1 m between them, the system loses approximately 50 cm of precision. Finally,
Table 8 allows us to conclude that Kalman filtering clearly improves the
precision of the system and, among the different spatial diversity techniques,
in this specific scenario, the filtered version of MRC is the most accurate
technique.

Table 8: Mean error (in meters) of the different multi-antenna techniques for the active system. Max. Distance Technique | MRC | Filt. MRC | SC | Filt. SC | SSC | Filt. SSC | ScanC | Filt. ScanC
---|---|---|---|---|---|---|---|---
10 m | 0.9057 | 0.6212 | 1.6425 | 1.1268 | 1.0485 | 1.1008 | 1.0060 | 0.5650
16 m | 3.5396 | 2.9744 | 4.8282 | 4.0904 | 3.5497 | 3.2720 | 4.0793 | 3.5286
17 m | 3.8662 | 3.3645 | 5.3449 | 4.6672 | 3.8546 | 3.422 | 4.5838 | 4.1054
Figure 20: Multi-antenna technique stability with and without Kalman
filtering.

####  5.4.5 Key findings

The results obtained for the active RFID system while transmitting in the pipe
workshop in the best case scenario (with LOS) showed that the tags offer long
range readings. This means that the system is suitable for monitoring in real-
time wide areas of the workshop with just one reader (what also decreases the
deployment costs).

However, although the accuracy of the RSS-based location system is roughly 1 m
for distances of up 10 m when applying multi-antenna techniques and Kalman
filtering, it increases rapidly with further distances, what indicates that is
necessary to make use of more sophisticated positioning algorithms.

The accuracy values obtained are in the same order of magnitude to those found
in the literature (the most relevant examples can be seen in Table 9). These
examples can be examined as evidence of the behavior of the technologies in
non-adverse environments, mainly offices and university buildings.
Nevertheless, direct comparison with the behavior of the smart pipe system
proposed is difficult because a coherent methodology does not exist between
the different works: various technologies are applied, different algorithms or
techniques, or distinct beacon spatial density. One of the main factors that
differentiate the environment presented with the ones exhibited by other
publications is that the experimental conditions of our proposal, although
sometimes similar regarding the size of the scenario, are quite adverse due to
the massive presence of metals, thus, facing a truly challenging deployment.

Table 9: Main features of state-of-the-art indoor positioning systems.

System | Scenario | Technology | Accuracy (average error)
---|---|---|---
LIPS LIPS  | Buildings | Wi-Fi |  $\thicksim$0.76 m
SDM SDM  | Small building (598 m2) | Wi-Fi |  $\thicksim$3 m
OIL OIL  | Area with four rooms | Wi-Fi | Order of meters
EZ EZ  | Small (486 m2) and big buildings (12.600 m2) | Wi-Fi |  $\thicksim$ 2 m (small) and 7 m (big)
WiGEM WiGEM  | Small (600 m2) and medium (3.250 m2) buildings | Wi-Fi | <8 m
WILL WILL  | Medium academic buildings (1.600 m2) | Wi-Fi | 86% room level accuracy
UnLoc UnLoc  | Different setups (largest 4.000 m2) | Wi-Fi and inertial sensors |  $\thicksim$1.69 m
Zee Zee  | Medium sized building (2.275 m2) | Wi-Fi |  $\thicksim$3 m together with EZ or Horus.
LiFS LiFS  | Medium sized building (1.600 m2) | Wi-Fi | 89% room level accuracy
Walkie-Markie Walkie-Markie  | Medium size office (3.600 m2) and shopping mall | Wi-Fi |  $\thicksim$ 1.65 m
RADAR RADAR  | Area 975 $m^{2}$, more than 50 rooms | Wi-Fi |  $\thicksim$ 3-5 m
Horus Horus  | 4th floor of the Computer Science building | Wi-Fi |  $\thicksim$ 3-5 m
AiRISTA FlowAiRISTA  | Asset tracking | Wi-Fi |  $\thicksim$ 1 m
IZat IZat  | Automotive | Wi-Fi, GPS, 4G |  $\thicksim$ 5 m
Ubisense Ubisense  | Smart factory | UWB |  $\thicksim$ 15 cm
Dart Dart  | Manufacturing | UWB |  $\thicksim$30 cm
3D-LOCUS 3D-LOCUS  | Laboratory | Ultrasound |  $\thicksim$ 8 mm
Elpas Elpas  | Healthcare and commercial | IR, UHF and LF RFID |  $\thicksim$ 1 m
SpotON SpotON  | Laboratory | Active RFID |  $\thicksim$ 3 m
Topaz TOPAZ  | Areas of $\thicksim$1000 m2 | Bluetooth+IR | 2-3 m
LandmarcLandmarc  | Laboratory | Active RFID | < 2 m
Sparse distributionsparse  | Not specified | Passive RFID | < 10 cm
GPs GPPS  | Area of 1600 m2 (55 rooms) | Active RFID |  $\thicksim$ 1.5 m
Robotics-based Robot  | 3rd floor of Duncan Hall at Rice University | Wi-Fi |  $\thicksim$ 1.5 m
MultiLoc MultiLoc  | 4th floor (more than 10 rooms) | Wi-Fi |  $\thicksim$ 2.7 m
Zigbee IPS Zigbee  | Engineering building 7.26$\times$16.5 m | Zigbee |  $\thicksim$ 3.01 m
TIX TIX  | Office (1020 m2) | Wi-Fi |  $\thicksim$ 5.4 m
BLE fingerprintingBLE  | Office (600 m2) | BLE | < 2.6 m, high beacon density
GSM fingerprinting GSMfingerprinting  | Large multi-floor buildings | GSM |  $\thicksim$ 5 m
NDI Optotrak  | Industry and healthcare | Infrared (3D) | 0.1 mm
IRIS_LPS IRIS_LPS  | Lecture hall (100 m2) | Infrared + stereo camara | < 16 cm
Bat Bat  | Building (1000 m2), three floors | Ultrasound | 3 cm
Cricket Cricket  | Different rooms | RF + Ultrasound | 10 cm, orientation accuracy 3$\degree$
Sonitor Sonitor  | Healthcare | Ultrasound,Wi-Fi and LF | Room level accuracy
COMPASS COMPASS  | Office building (312 m2) | Wi-Fi, no real-time tracking |  $\thicksim$ 1.65 m
OPT OPT  | Context aware app in personal network | IEEE 802.15.4 | 1.5-3.8 m
Easy Living EasyLiving  | In-home and in-offices | Multiple tech | Accuracy non guaranteed
Beep Beep  | Room 20x9 m: users can use their own devices | Sound source location | 0.4 cm

###  5.5 Display module of the smart pipe system

Once verified that constant monitoring can be only achieved with an active
RFID system, additional tests were conducted for the ubiquitous real-time CPS
system while transmitting in the pipe workshop. The results obtained can be
seen by the operators thanks to the display module, which can be accessed
through any device with a regular web browser (i.e. from PCs, Macs,
smartphones, and tablets). The system shows the location of the pipes in the
workshop in real time in the way shown in Figure 21, where the blue circles
represent the pipes or a set of pipes within a radius of 2 m (this is useful
for pallets that carry several pipes).

Considering that numerous pipes are displayed while moving through the
workshop, a filter can be used to only show a specific pipe or a subset that
meet certain criteria. Moreover, operators can zoom in and out throughout the
floor map to watch specific areas.

Operators, in addition to viewing the pipes in a map, can access certain
information about them. This functionality is illustrated in Figure 22 and it
implies obtaining data on specific parameters (i.e. pipe identifier, area or
material).

Accuracy can also be studied from this display module: it can be observed the
position oscillations of the pipes due to the RSS noise. Such a noise is
drastically reduced with the techniques analyzed in the previous sections and
gives a really stable representation of the state of the pipe workshop in
locations with LOS.

Figure 21: Floor map of the workshop with located pipes (blue circles). Figure
22: Example of the information shown to the operators about the basic
characteristics of a pipe.

###  5.6 Automatic event detection using smart pipes

The concept of smart pipe is based on the fact that a pipe actually informs
through its communications transceiver signal level about its position. Such
an information, when accurate, allows for implementing a wide range of very
useful services. The services are provided by the business intelligence
module, which processes the position information and shows meaningful
notifications through the display module. Figure 23 illustrates an example of
a smart service: when a pipe crosses from one area to another, a pop-up is
shown on the upper right part of the screen indicating the event, the pipe
identifier, and a timestamp. This event warns the operators when a certain
pipe is coming towards their work area so that they are prepared to receive
it.

These notifications represent a simple but useful service. In future releases
the software will incorporate other services like the measurement of the time
spent by a pipe in certain areas (to create statistics about the performance
of the different areas/operators), the quantification of the level of
occupation of the stacking areas, or the possibility of triggering certain
automatic behaviors (for instance, through the workshop machinery and robots)
when a pipe reaches certain point.

Figure 23: Notifications shown on the right upper part when a pipe crosses
from one area to another.

##  6 Conclusions

This article described the selection and validation of the necessary
technology to perform improved traceability and location of the pipes used in
the construction of ships. The proposed smart pipe system is a novel example
of the benefits of CPS, thus providing a reliable remote monitoring platform
to leverage strategic applications and enhancements in the shipyard
environment. The system is based on the concept of smart pipe, a kind of pipe
able to transmit signals periodically that allows for providing useful
services in a shipyard. This paper conducted a detailed analysis of the
shipyard environment, the scenarios that involve pipes in their process, the
operational and technical requirements, and a feasibility study to choose the
best technology and communications architecture. Also, it has made a
comprehensive technology validation. Through multiple tests it was confirmed
that:

  * •

The passive RFID system, due to its limited range, is only suitable for
situations where the aim is to control the movement between areas, but it
would not be suitable for ubiquitous real-time control.

  * •

This passive system, when making use of an ’L-shaped’ array of antennas,
mitigates the low angle of reading, but it reduces the reading distance to
almost a half.

  * •

In the case of active RFID readers, their use allows for reaching longer
distances and get constant monitoring of tags in wide areas. However, the
longer the distance, the less accurate the RSS-based distance estimations are.

  * •

Multi-antenna algorithms and Kalman filtering help to stabilize RSS and
improve the accuracy of the positioning system.

Given all these factors, it is verified that the active RFID technology is the
best positioned technology for implementing the CPS proposed. Further research
needs to be carried out to explore even more accurate positioning algorithms
in order to minimize the influence of the interference caused by the
environment.

Although the present article was aimed at applying the latest research for
building a smart pipe system, it also included the following original
contributions, which have not been found together in the literature as of
writing:

  * •

It presented the novel concept of Shipyard 4.0, a shipyard built on the
application of the principles of Industry 4.0.

  * •

It described in detail how a shipyard pipe workshop works and what are the
requirements for building a smart pipe system.

  * •

It indicated how to build a positioning system from scratch in an environment
as harsh in terms of communications as a shipyard. Additionally, it was not
found in the literature any practical analysis on the application of RFID
technology in any similar application and scenario.

  * •

It defined the concept of smart pipe and showed an example of its
implementation and the architecture that surrounds it.

  * •

It proposed the use of spatial diversity techniques to stabilize RSS values.
It has not been found any other publication describing the application of such
techniques in RFID systems in the way it is proposed in this paper.

To summarize, this article presented the foundations for enabling an
affordable CPS for Shipyards 4.0. The system design proposed allows shipyards
to collect more information on the pipes and to make better use of it.
Furthermore, with this system working, the development of applications related
to the monitoring of elements different than pipes (e.g. wearables for
operators, tools, shared machines) is straightforward. Thus, the forthcoming
applications will enable the Shipyard 4.0 to leverage smarter energy
consumption, greater inbound/outbound logistics and information storage,
workforce safety and control, and real-time yield optimization.

###### Acknowledgements.

Acknowledgments: This work is part of the Pipe Auto-ID research line of the
Navantia-UDC Joint Research Unit and has been funded by it. Author
Contributions: Diego Noceda-Davila and Tiago M. Fernández-Caramés conceived
and designed the experiments; Diego Noceda-Davila and Paula Fraga-Lamas
performed the experiments; Paula Fraga-Lamas, Tiago M. Fernández-Caramés,
Manuel A. Díaz-Bouza, and Miguel Vilar-Montesinos analyzed the data; Paula
Fraga-Lamas, Tiago M. Fernández-Caramés, Diego Noceda-Davila, Manuel A. Díaz-
Bouza, and Miguel Vilar-Montesinos wrote the paper. Conflicts of Interest: The
authors declare no conflict of interest. The founding sponsors had no role in
the design of the study; in the collection, analyses, or interpretation of
data; in the writing of the manuscript, and in the decision to publish the
results.

## Abbreviations

The following abbreviations are used in this manuscript:

AoA: Angle of Arrival
BI: Business Intelligence
BLE: Bluetooth Low Energy
C4ISR: Command, Control, Communications, Computers, Intelligence, Surveillance
and Reconnaissance
CPS: Cyber-Physical Systems
EGC: Equal Gain Combiner
HCI: Human-Computer Interfaces
IoT: Internet of Things
LAN: Local Area Network
LOS: Line Of Sight
RFID: Radio Frequency IDentification
REST: REpresentational State Transfer
RSS: Received Signal Strength
MES: Manufacturing Execution System
MIMO: Multiple-input Multiple-output
MRC: Maximum-Ratio Combiner
MSE: Minimum-Squared Error
SC: Selection Combiner
ScanC: Scanner Combiner
SSC: Switch-and-Stay Combiner
TDoA: Time Different of Arrival
ToA: Time of Arrival
ToF: Time of Flight
WPAN: Wireless Personal Area Network
WSN: Wireless Sensor Networks

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