**3. Experimental study**

Data RFID Reader RFID Tag

Amongst RFID technologies, Ultra Wide Band (UWB) is the most accurate and fault tolerant

RFID-UWB is an emerging radio technology marked by accuracy in the estimation of the po‐

According to the most influential and widespread definition, provided by the *Federal Com‐ munications Commission Regulation* [26], an RFID-UWB system is defined as any intentional radiator having a fractional bandwidth greater than 20% or an absolute bandwidth greater than 500 MHz. These requirements mean that a band-limited signal, with lower frequency fL and upper frequency fH, must satisfy at least one of the following conditions (Equation 1, 2):

According to [27], the main characteristics of an RFID-UWB are the transmission of a signal over multiple frequency bands simultaneously and the brief duration of that transmission. RFID-UWB requires a very low level of power and can be used in close proximity to other RF signals without causing or suffering interferences. At the same time, the signal passes easily through walls, equipment and clothing [27-29] and more than one position can be tracked simultaneously. Moreover, RFID-UWB systems overcome limitations due to reflec‐ tion, refraction, and diffraction phenomena, using pulses for the broadband transmission. The use of RFID-UWB offers other advantages, such as no line-of-sight requirements, high accuracy and resolution, lighter weight (the weight for each tag is less than 12 g) and the possibility to trace multiple resources at the same time, real-time and three-dimensionally. Furthermore, RFID-UWB sensors are cheaper, which make the RFID-UWB positioning sys‐

An RFID-UWB system comprises a computer and a hub (including a graphical interface), RFID-UWB sensors to record signals in real-time, RFID-UWB tags at low and high power

Host computer

**Figure 3.** Components of an RFID system ([6] version modified by [25])

system. It can have a widespread usage in indoor localizations.

sition, and the precision with which it is possible to obtain that accuracy.

2( *f <sup>L</sup>* - *f <sup>H</sup>* )

*2.3.1. RFID – Ultra Wide Band (UWB)*

350 Radio Frequency Identification from System to Applications

tem a cost-effective solution.

Coupling element (coil, antenna)

( *<sup>f</sup> <sup>L</sup>* <sup>+</sup> *<sup>f</sup> <sup>H</sup>* ) >20*%* (1)

*f <sup>L</sup>* - *f <sup>H</sup>* >500 *MHz* (2)

In this section, the experimental study about the traceability of material flows through IPS system based on RFID-UWB technology and its results are presented.

#### **3.1. Components of the RFID-UWB system**

The authors chose the RFID-UWB system, among IPS technologies since it is able to ensure the highest accuracy and precision in the measurements thanks to the combined use of AOA and TDOA techniques. The system comprises sensors, tags, and the software location plat‐ form, described below.

	- **◦** *Reactivity in real-time*: each sensor maintains a constant frequency of 160 Hz, which means the tag can be seen every 6.25 ms by each sensor;
	- **◦** *Flexible installations*: this kind of infrastructure can be used for both small and large in‐ stallations. Several sensors can be integrated in a unique system to monitor a big area and manage a large number of tags simultaneously;
	- **◦** *Synchronism:* in order to guarantee synchronism, the sensors are cabled with CAT-5 ca‐ bles. A cell made up of several sensors is able to cover 10,000 m2 of environment. In order to extend the covered area, the cells can be connected to each other;
	- **◦** *Bidirectional communication:* the sensors support bidirectional communication at 2.45 GHz. This allows the system to dynamically manage tags in an optimal way;
	- **◦** *Connectionsof sensors:* the sensors can be connected with standard Ethernet cables or through wireless adaptors, using pre-existing infrastructures like access point, switch Ethernet and CAT-5 wiring for communication between the sensors and the server;

**◦** *Ease of maintenance:* the sensors are managed in a remote way through TCP/IP protocols and standard Ethernet for communication and configuration.

tions can use tag localization to work according to the events. The application can send

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**◦** *Resistant and suitable*: tags are resistant in critical industrial environments, since they can withstand dust and water. They can also be installed in mechanical and electronic

**◦** *Battery life:* the techniques of low-consumption and power management affect the dura‐ tion of the battery. In a typical application, in which a tag is used to identify an opera‐

**Figure 5.** Compact tags (on the left) and slim tags (on the right), used in the experimental application (courtesy of

**•** *Software Location Platform* is used to control and calibrate the system, to manage the loca‐ tions of data generated by tags and received by sensors and to analyse, communicate and inform users on the data system. The software platform is made up of the *Location Engine*

**•** *Location Platform* is a software that collects and processes data from sensors and tags, viewable thanks to a graphical interface. In this way, it is possible to obtain 2D and 3D maps of the environment and detected assets. The collected data can be sent to other sys‐

**•** *Location EngineCalibration* (*LEC*) allows the sensors to be set, calibrated, and configured in cells using a graphical user interface. It is designed to allow the simple coordination of data from sensors and tags in order to be integrated in other applications. The Location Engine is the base component of the software platform since it allows the creation and loading of maps, single cell creation and setup of tags and sensors (deciding master and slave sensors), and the calibration of the system sensitivity (fixing the "noise threshold"). **•** The Location Engine supports several algorithms to determine tag position through sen‐ sor measurements. Each algorithm has a set of parameters that regulate tags behaviour. These parameters are called *filters* and can be applied to a single tag or a group of tags. The Location Engine presents one algorithm without a filter and another four filtered al‐

tems for further analysis and stored within the platform to act as a database.

feedback to the user through LEDs or acoustic signals;

tor every 3 sec, the battery has an average duration of four years.

Figure 5 shows an example of compact tags (on the left) and slim tags (on the right).

instrumentation safely;

Ubisense Group plc)

gorithms:

*Calibration* and *Location Platform*.

Figure 4 shows the sensors used in the experimental application.

**Figure 4.** Sensors used in the experimental application (courtesy of Ubisense Group plc)

	- **◦** *Precise localization*: the tag transmits RFID-UWB radio pulses, used by the localization system for defining the tag position within 15 cm. The precision of the system is also maintained in complex indoor environments thanks to RFID-UWB technology. In this way it is possible to obtain accurate information on 3D positions even when the tag is detected by only two sensors;
	- **◦** *Bidirectional communication*: tags use a dual-radio system in addition to the mono-direc‐ tional RFID-UWB radio communication, used for the spatial detection. The capacity of bidirectional communication allows the system to dynamically manage the update rate of tags, control of LEDs and battery status;
	- **◦** *Flexible update rate*: the software platform allows the update rate of tags to be varied. If a tag moves quickly, it can have high upgrading for more precise localization; instead, if it moves slowly the update rate could be reduced in order to save the battery. When the tag is at rest, it is put into energy saving mode thanks to a built-in motion sensor that allows restart in case of movement;
	- **◦** *Interactivebuttons*: slim tags have two buttons (while compact tags have only one but‐ ton) to allow context-sensitive inputs in systems requiring interactivity. The applica‐

tions can use tag localization to work according to the events. The application can send feedback to the user through LEDs or acoustic signals;


Figure 5 shows an example of compact tags (on the left) and slim tags (on the right).

**◦** *Ease of maintenance:* the sensors are managed in a remote way through TCP/IP protocols

**•** *Tags:* these are small and robust devices worn by a person or attached to an object to be accurately located within an indoor environment. Tags transmit brief RFID-UWB pulses that are received by sensors and are used to determine their position. The use of RFID-UWB pulses ensures both high precision (approximately 15 cm) and great reliability in complex indoor environments, characterized by noises like reflection from walls or the presence of metallic objects in indoor environments. Each tag is made up of movement detectors for instantaneous activation, LEDs for identification and buttons for executing

**◦** *Precise localization*: the tag transmits RFID-UWB radio pulses, used by the localization system for defining the tag position within 15 cm. The precision of the system is also maintained in complex indoor environments thanks to RFID-UWB technology. In this way it is possible to obtain accurate information on 3D positions even when the tag is

**◦** *Bidirectional communication*: tags use a dual-radio system in addition to the mono-direc‐ tional RFID-UWB radio communication, used for the spatial detection. The capacity of bidirectional communication allows the system to dynamically manage the update rate

**◦** *Flexible update rate*: the software platform allows the update rate of tags to be varied. If a tag moves quickly, it can have high upgrading for more precise localization; instead, if it moves slowly the update rate could be reduced in order to save the battery. When the tag is at rest, it is put into energy saving mode thanks to a built-in motion sensor

**◦** *Interactivebuttons*: slim tags have two buttons (while compact tags have only one but‐ ton) to allow context-sensitive inputs in systems requiring interactivity. The applica‐

and standard Ethernet for communication and configuration.

Figure 4 shows the sensors used in the experimental application.

352 Radio Frequency Identification from System to Applications

**Figure 4.** Sensors used in the experimental application (courtesy of Ubisense Group plc)

particular operations. The main characteristics of tags are:

detected by only two sensors;

of tags, control of LEDs and battery status;

that allows restart in case of movement;

**Figure 5.** Compact tags (on the left) and slim tags (on the right), used in the experimental application (courtesy of Ubisense Group plc)


**•** *No filtering algorithm*: in this configuration, no filters are applied. This means that the posi‐ tion is evaluated only by measuring AOA and TDOA at a specific moment. In this way, any previous data is not processed and the path and speed of movements are not consid‐ ered. Not using filters does not allow optimal measurements to be obtained.

**•** *Low support reset count*: defines the time in which the tag can be seen with low support modality (the situation in which the measurements rejected by the filter are more than the

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**•** *Min reset measurements*: indicates the minimum number of support measurements before

**•** *Tag power class*: the filter can validate the tag's position based on the level of signal power received by each sensor. The filter has to recognize the type of tag that sends the signal, so as to interpret the received power correctly. The value 0 disables the function, value 1 in‐ dicates a compact standard tag and value 2 indicates a tag with amplified signal power;

**•** *Static distance*: describes the minimum distance travelled by a tag compared with the last

**•** *Static alpha*: defines the fraction (0.0 – 0.1) of the current measurement used by the filter

**•** *Max valid position variance*: identifies the maximum variance in estimating the position. This value has to be less than or equal to the "max position variance". The difference be‐ tween these two parameters is that if the uncertainty is higher than the max position var‐ iance, the forecast of the next localization does not change; while if the variance is higher than the max valid position variance, the filter will continue to track the position, but this

**•** *Horizontal velocity standard deviation*: the filter operates with a model of movement in which the tag velocity is considered constant. This parameter indicates the rate of velocity

**•** *Vertical velocity standard deviation*: similar to the horizontal velocity standard deviation,

**•** *Vertical position standard deviation* (only for the filter with prefixed height): although the height is fixed, this parameter allows the tag to be varied along the vertical movement. If

**•** *Tag height above cell floor* (only for filter with prefixed height): fixes the value of height *Z*

the value of this parameter is 0, the tag will only be detected in two dimensions;

when the tag is considered stable. The tag position is computed as follows:

**•** If alpha is close to 0.0, the movement of the tag will be significantly damped;

**•** *Max velocity*: identifies the maximum velocity at which the object can move;

**•** *Max position variance*: describes the maximum variation in estimating the position;

**•** (alpha \* current position) + (1.0 – alpha) \* (last position)

No-static algorithms can regulate other parameters such as:

measurement is not considered valid.

increase in *X* and *Y* as the time varies;

but for the vertical velocity;

where the tag should always be.

Static algorithms can also regulate another parameter:

valid ones) before the reset of the filter;

the reset of the filter;

one;


The parameters that can be regulated by the filtered algorithms are:


**•** *No filtering algorithm*: in this configuration, no filters are applied. This means that the posi‐ tion is evaluated only by measuring AOA and TDOA at a specific moment. In this way, any previous data is not processed and the path and speed of movements are not consid‐

**•** Filtered algorithms try to interpret tag movements to predict their positions during fur‐ ther measurements. Information coming from AOA and TDOA techniques is analysed and compared with the expected position that will be used in further measurement. The filter can eliminate measurements that can be deteriorated by reflections or disturbed by external noises. In order to do so, it is necessary to identify a movement pattern for the filter that defines the limitations to which the measured object has to be subjected. The higher the number of applied limitations, the better the robustness of the measurement.

**•** *Information filter*: the tag can move along three directions but, if it is not seen for a period, the movement pattern assumes that it is continuing to move according to the last speed value and along the last detected direction. This algorithm is used for assets that move

**•** *Fixed height information filter*: the tag is free to move horizontally, but the vertical move‐ ments have to remain close to a predetermined threshold height. In this case, if contact with the tag is lost, it is assumed that it continues to move with equal speed along the horizontal direction, remaining close to the vertical predetermined height. Like the previ‐ ous algorithm, the level of uncertainty of the location increases with the time. This algo‐

**•** *Static information filter*: the tag is free to move in three directions. If the tag is not detected, its position is identified with the last one and the level of uncertainty of localization in‐ creases with the time. This algorithm is used for assets that do not normally move or move in an unpredictable way, such as operators. The algorithm does not have any spa‐ tial limitations, allowing the detection of 3D movements (for example the movement of

**•** *Static fixed height information filter:* the tag is free to move horizontally, but it is limited to the vertical direction. If the tag is not seen, it is assumed that its position is the last one detected and the height is close to the prefixed limit. This algorithm is used for targets that do not normally move or move in unpredictable way. Because of its vertical limita‐

**•** *Handover stickiness*: indicates the tag's adherence to the cell in which it is located. It is measured indicating the maximum number of failed measurements of the tag's position

**•** *Handover minimum sensor count*: describes the minimum number of sensors belonging to

tion, it is used for vehicles, tools, and people that move in two dimensions.

The parameters that can be regulated by the filtered algorithms are:

rithm is mainly used for vehicles moving at high speed and in two directions;

ered. Not using filters does not allow optimal measurements to be obtained.

The filtered algorithms are presented below:

354 Radio Frequency Identification from System to Applications

people climbing the stairs);

before considering it out of the cell;

the cell;

with predictable speed and without direction limitations;


No-static algorithms can regulate other parameters such as:


Static algorithms can also regulate another parameter:

**•** *Horizontal position standard deviation*: the filter operates according to the movement pattern in which the tag's position is considered constant. The uncertainty of the tag's position in‐ creases with the time although the tag's forecasting continues to be in the last position. This parameter identifies the increasing rate of standard deviation of position in *X* and *Y* as the time varies.

It is possible to underline the difference between static and dynamic filtering algorithms. In the case of dynamic filter, there are long straight lines that identify the moments in which the sensors lose track of the tag and find it again few moments later. Consequently, the measurement's accuracy is low, mainly in the computing of distances travelled, which may be compromised. In the case of static filter, the traced path is very close to the real one, with‐ out straight lines, since the tag is always under control. Figure 6 shows an example of track‐ ing of the same path using a dynamic filtering algorithm (on the left) and a static filtering algorithm (on the right).

**Figure 7.** Map (on the left) and map (on the right) of the indoor environment considered in the application

**00:11:CE:00:40:A7 (master)** 15.618 -0.582 4.336 **00:11:CE:00:41:4C (slave)** 30.868 11.945 4.545 **00:11:CE:00:41:64 (slave)** 13.085 18.898 4.336 **00:11:CE:00:41:92 (slave)** -0.308 11.039 4.651 **STA (reference point)** 15.409 10.833 2.100

**Sensors name X [m] Y [m] Z [m]**

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The coordinates of sensors are presented in Table 1:

**Table 1.** Coordinates of sensors

**Figure 8.** Connection of sensors with the system

**Figure 6.** Path traced with dynamic filtering algorithm (on the left) and static filtering algorithm (on the right)

 Figure 6. Path traced with dynamic filtering algorithm (on the left) and static filtering algorithm (on the right)

#### **3.2. Installation and calibration of the system**

Figure 19. a. Path travelled using static filter b. Path travelled using dynamic filter

Figure 20. a. Path travelled using static filter b. Path travelled using dynamic filter

 The authors decided to install an IPS experimental system based on RFID-UWB technology in the Laboratory of Manufacturing System of Bologna University that, thanks to the presence of walls, machinery and metal objects, could be representative of a real industrial application.

Figure 7 shows the 2D map (on the left) and the 3D map (on the right) (obtained by LEC plat‐ form) of the laboratory, where the white squares indicate the position of the sensors. The opti‐ mal configuration needs sensors to be installed in the four corners of the building, but in actual fact, because of the presence of obstacles in the corners of the laboratory, the sensors are installed according to a rhombus distribution, able to guarantee total coverage of the area.

(a) (b)

(a) (b)

**•** *Horizontal position standard deviation*: the filter operates according to the movement pattern in which the tag's position is considered constant. The uncertainty of the tag's position in‐ creases with the time although the tag's forecasting continues to be in the last position. This parameter identifies the increasing rate of standard deviation of position in *X* and *Y*

It is possible to underline the difference between static and dynamic filtering algorithms. In the case of dynamic filter, there are long straight lines that identify the moments in which the sensors lose track of the tag and find it again few moments later. Consequently, the measurement's accuracy is low, mainly in the computing of distances travelled, which may be compromised. In the case of static filter, the traced path is very close to the real one, with‐ out straight lines, since the tag is always under control. Figure 6 shows an example of track‐ ing of the same path using a dynamic filtering algorithm (on the left) and a static filtering

 Figure 6. Path traced with dynamic filtering algorithm (on the left) and static filtering algorithm (on the right)

**Figure 6.** Path traced with dynamic filtering algorithm (on the left) and static filtering algorithm (on the right)

The authors decided to install an IPS experimental system based on RFID-UWB technology in the Laboratory of Manufacturing System of Bologna University that, thanks to the presence of walls, machinery and metal objects, could be representative of a real industrial application.

Figure 7 shows the 2D map (on the left) and the 3D map (on the right) (obtained by LEC plat‐ form) of the laboratory, where the white squares indicate the position of the sensors. The opti‐ mal configuration needs sensors to be installed in the four corners of the building, but in actual fact, because of the presence of obstacles in the corners of the laboratory, the sensors are installed according to a rhombus distribution, able to guarantee total coverage of the area.

(a) (b)

(a) (b)

**3.2. Installation and calibration of the system**

Figure 19. a. Path travelled using static filter b. Path travelled using dynamic filter

Figure 20. a. Path travelled using static filter b. Path travelled using dynamic filter

as the time varies.

356 Radio Frequency Identification from System to Applications

algorithm (on the right).

**Figure 7.** Map (on the left) and map (on the right) of the indoor environment considered in the application

The coordinates of sensors are presented in Table 1:


**Table 1.** Coordinates of sensors

**Figure 8.** Connection of sensors with the system

The sensors have to be located as close as possible to the ceiling of the building to guarantee maximum coverage of the space and their angulation has to be directed towards the centre of the building. The sensors are grouped into rectangular cells, where they are connected to the switch POE that guarantees the power that is in turn linked with the PC (Figure 8). Each cell is characterized by a main sensor (master) that coordinates the activities of the other sen‐ sors (slave) and communicates with the tags. The master sensor has to be connected with the slaves by CAT-5 cables (Figure 9), in order to ensure the time synchronization. When the connection is made, the Location Engine Configurator is set to "Running" mode and the sys‐ tem is ready to work.

**Figure 10.** Calibration of sensors

**Figure 11.** Calibration of cables

The system has to be connected with the layout of the environment to be monitored. A map of the laboratory has to be created, according to the external and internal walls, the columns

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and any other architecture present in the laboratory (Figure 12).

**Figure 12.** Map of the area controlled by the proposed system

**Figure 9.** Connection between master and slave sensors

The threshold level of the "background noise" has to be decided, so to allow the system to distinguish valid signals from environmental noises. In order to calibrate the sensors, the power level detected by them is measured, verifying that the "background noise" remains below the threshold level. After that, it is possible to calibrate the sensor orientation. The sensors are oriented to a known tag, taken as reference. Figure 10 shows the sensor calibra‐ tion through AOA. The green lines connect each sensor to the detected position of the tag.

In order to activate the localization through TDOA, it is necessary to calibrate cables that synchronize all the slave sensors with the master. When the cable calibration is completed, blue strips are added to the green lines, one for each pair of sensors. In absence of obstacles, assets, and reflection phenomena, the blue lines are straight; in actual fact, they are curved lines, with increased bending as interferences and noises increase (Figure 11).

**Figure 10.** Calibration of sensors

The sensors have to be located as close as possible to the ceiling of the building to guarantee maximum coverage of the space and their angulation has to be directed towards the centre of the building. The sensors are grouped into rectangular cells, where they are connected to the switch POE that guarantees the power that is in turn linked with the PC (Figure 8). Each cell is characterized by a main sensor (master) that coordinates the activities of the other sen‐ sors (slave) and communicates with the tags. The master sensor has to be connected with the slaves by CAT-5 cables (Figure 9), in order to ensure the time synchronization. When the connection is made, the Location Engine Configurator is set to "Running" mode and the sys‐

The threshold level of the "background noise" has to be decided, so to allow the system to distinguish valid signals from environmental noises. In order to calibrate the sensors, the power level detected by them is measured, verifying that the "background noise" remains below the threshold level. After that, it is possible to calibrate the sensor orientation. The sensors are oriented to a known tag, taken as reference. Figure 10 shows the sensor calibra‐ tion through AOA. The green lines connect each sensor to the detected position of the tag.

In order to activate the localization through TDOA, it is necessary to calibrate cables that synchronize all the slave sensors with the master. When the cable calibration is completed, blue strips are added to the green lines, one for each pair of sensors. In absence of obstacles, assets, and reflection phenomena, the blue lines are straight; in actual fact, they are curved

lines, with increased bending as interferences and noises increase (Figure 11).

tem is ready to work.

358 Radio Frequency Identification from System to Applications

**Figure 9.** Connection between master and slave sensors

**Figure 11.** Calibration of cables

The system has to be connected with the layout of the environment to be monitored. A map of the laboratory has to be created, according to the external and internal walls, the columns and any other architecture present in the laboratory (Figure 12).

**Figure 12.** Map of the area controlled by the proposed system

When the map is loaded into the system, the coordinates of the sensors' position and some reference points within the area to be monitored have to be determined. A corner of the building is identified as the axis origin and is indicated by (0;0;0); the other corners will be identified with (X;0;0) and (0;Y;0) where X and Y are the length of the building sides. The level of floor is set as Z=0 so to use the 3D localization capacity of the system. In order to connect the position of the sensors with the laboratory's corner coordinates, the object locali‐ zations have to be calibrated, using known points as references. After that, the software will provide a 3D image of the area to be monitored.

**Test 1**

**•** Filter: *Static Fixed Height Information Filter*;

**•** Update each four time slot;

**Figure 13.** Reference points for static tests

between them.

**Table 2.** Analysis of static *Test 1*

Table 2 presents the estimated and detected coordinates of the 16 points, specifying the error

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**Point X [m] Y [m] X detected [m] Y detected [m] Error [m]** 5.826 11.113 6.1007 11.0112 0.2930 7.976 10.991 7.6250 10.7717 0.4138 11.389 11.207 11.5024 11.0014 0.2347 15.693 7.138 15.9791 7.2085 0.2946 18.627 7.138 18.6374 7.1973 0.0602 26.39 7.138 26.1077 7.4651 0.4320 26.39 11.204 26.5720 10.8224 0.4227 26.39 14.028 26.2713 13.4417 0.5980 26.39 16.6 29.1303 18.6123 3.3998 19.347 11.207 19.5315 10.5318 0.6999 8.17 7.113 7.8618 7.2394 0.3330 3.415 7.05 3.7401 8.0534 1.0548 3.399 15.739 3.3871 14.3100 1.4289 16.397 2.748 14.9795 3.2646 1.5085 11.367 2.748 11.7532 4.3628 1.6602 15.637 15.187 15.8656 14.9317 0.3426

**•** Frequency: 37 Hz.

In order to complete the calibration and verify the absence of errors, it is important to test the system, moving a tag within the area and ensuring that the sensors work correctly and that all necessary data is displayed.

#### **3.3. Experimental evidence**

The experimental research consists of several tests, static and dynamic.

The static tests consist of the identification of different points (to which tags are applied) within the area to be monitored. The sensors have to detect the coordinates of the tags to compare the estimated and detected coordinates of every point.

The dynamic tests consist of the application of a tag to an operator that goes around the monitored area. The operator follows prefixed paths, and the route and distance travelled by him are compared with the estimated values, measured in advance.

#### *3.3.1. Static tests*

In order to undertake the accuracy and precision of the proposed RFID-UWB system, the first test is the measurement of known point coordinates through a laser. 16 points within the monitored area, chosen according to the characteristics of visibility, proximity to metal objects and position, are identified.

The static tests are performed according to the variation of some tag parameters, such as:


Figure 13 shows the considered 16 points represented in the map of the laboratory.

For each point, four tests are performed in order to understand the average error between the estimated and detected coordinates.

#### **Test 1**

When the map is loaded into the system, the coordinates of the sensors' position and some reference points within the area to be monitored have to be determined. A corner of the building is identified as the axis origin and is indicated by (0;0;0); the other corners will be identified with (X;0;0) and (0;Y;0) where X and Y are the length of the building sides. The level of floor is set as Z=0 so to use the 3D localization capacity of the system. In order to connect the position of the sensors with the laboratory's corner coordinates, the object locali‐ zations have to be calibrated, using known points as references. After that, the software will

In order to complete the calibration and verify the absence of errors, it is important to test the system, moving a tag within the area and ensuring that the sensors work correctly and

The static tests consist of the identification of different points (to which tags are applied) within the area to be monitored. The sensors have to detect the coordinates of the tags to

The dynamic tests consist of the application of a tag to an operator that goes around the monitored area. The operator follows prefixed paths, and the route and distance travelled

In order to undertake the accuracy and precision of the proposed RFID-UWB system, the first test is the measurement of known point coordinates through a laser. 16 points within the monitored area, chosen according to the characteristics of visibility, proximity to metal

The static tests are performed according to the variation of some tag parameters, such as:

**•** Filter used (*No-filter, Information Filter, Fixed Height Information Filter, Static Information Fil‐*

**•** All tests are performed by putting the asset on a support 0.5 m high, except point 13,

For each point, four tests are performed in order to understand the average error between

Figure 13 shows the considered 16 points represented in the map of the laboratory.

The experimental research consists of several tests, static and dynamic.

by him are compared with the estimated values, measured in advance.

compare the estimated and detected coordinates of every point.

provide a 3D image of the area to be monitored.

360 Radio Frequency Identification from System to Applications

that all necessary data is displayed.

objects and position, are identified.

**•** Update each four time slot;

which is placed 2 m high.

the estimated and detected coordinates.

**•** Frequency: 37 Hz;

*ter, Static Fixed Height Information Filter*);

**3.3. Experimental evidence**

*3.3.1. Static tests*


**Figure 13.** Reference points for static tests

Table 2 presents the estimated and detected coordinates of the 16 points, specifying the error between them.


**Table 2.** Analysis of static *Test 1*

The average error of *Test 1* is 0.8236 m.

The points situated in the best positions (excluding points 9, 12, 13, 14 and 15) are located with high accuracy and present an average error of 37 cm. The worst result of point 9 is due to the presence of numerous obstacles around the considered area that make the tag visible to only one sensor. The same causes also influence the detection of points 12, 13, 14 and 15, although with lower impact.

**Test 3**

**•** Filter: *Static Information Filter*; **•** Update each four time slot;

Table 4 presents the estimated and detected coordinates of the 16 points, specifying the error

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**Point X [m] Y [m] X detected [m] Y detected [m] Error [m]** 5.826 11.113 6.0484 10.8824 0.3203 7.976 10.991 7.4876 11.0006 0.4884 11.389 11.207 11.3885 10.9646 0.2423 15.693 7.138 15.9165 6.9975 0.2640 18.627 7.138 18.6268 7.0524 0.0855 26.39 7.138 21.5961 4.4598 5.4912 26.39 11.204 26.5539 10.9868 0.2720 26.39 14.028 25.7937 13.2303 0.9959 26.39 16.6 22.0670 9.2817 8.4996 19.347 11.207 20.3456 10.0280 1.5450 8.17 7.113 7.1760 7.0652 0.9950 3.415 7.05 3.4986 7.7185 0.6737 3.399 15.739 3.1959 14.0444 1.7067 16.397 2.7481 14.9009 2.8403 1.4989 11.367 2.7481 12.5534 3.8802 1.6399 15.637 15.187 15.8690 14.8410 0.4165

Like the other two tests, points 6 and 9 present largely incorrect values, because of the con‐ dition of the area in which they are located. The other values are in line with the estimated

Table 5 presents the estimated and detected coordinates of the 16 points, specifying the error

**•** Frequency: 37 Hz.

**Table 4.** Analysis of static *Test 3*

**•** Filter: *Information Filter*;

**•** Frequency: 37 Hz.

between them.

**•** Update each four time slot;

measurements.

**Test 4**

The average error of *Test 3* is 1.5709 m.

between them.

#### **Test 2**


Table 3 presents the estimated and detected coordinates of the 16 points, specifying the error between them.


**Table 3.** Analysis of static *Test 2*

The average error of *Test 2* is 1.661 m.

The absence of filters means that the oscillations of the tag positions are not damped. This leads to the worst result of all the tests. It is possible to note that the easily reachable and visible points present low error values, while for the most critical points the system per‐ formance is worse, even reaching high error values (in the order of metres).

#### **Test 3**

The average error of *Test 1* is 0.8236 m.

362 Radio Frequency Identification from System to Applications

although with lower impact.

**•** Filter: any filter applied;

**•** Frequency: 37 Hz.

**Table 3.** Analysis of static *Test 2*

The average error of *Test 2* is 1.661 m.

between them.

**•** Update each four time slot;

**Test 2**

The points situated in the best positions (excluding points 9, 12, 13, 14 and 15) are located with high accuracy and present an average error of 37 cm. The worst result of point 9 is due to the presence of numerous obstacles around the considered area that make the tag visible to only one sensor. The same causes also influence the detection of points 12, 13, 14 and 15,

Table 3 presents the estimated and detected coordinates of the 16 points, specifying the error

**Point X [m] Y [m] X detected [m] Y detected [m] Error [m]** 5.826 11.113 5.8597 10.9974 0.1203 7.976 10.991 7.9914 11.1263 0.1362 11.389 11.207 11.4886 11.1252 0.1289 15.693 7.138 15.8387 7.2311 0.1729 18.627 7.138 18.4915 7.0736 0.1499 26.39 7.138 21.3617 3.6867 6.0987 26.39 11.204 28.4824 11.4226 2.1038 26.39 14.028 25.6281 13.6126 0.8676 26.39 16.6 28.6270 9.2454 7.6872 19.347 11.207 19.4776 10.3303 0.8863 8.17 7.113 7.7177 7.6014 0.6656 3.415 7.05 6.5425 5.6330 3.4335 3.399 15.739 3.5185 14.9626 0.7855 16.397 2.748 14.7514 2.9406 1.6567 11.367 2.748 10.9230 4.2645 1.5800 15.637 15.187 15.7053 15.1101 0.1028

The absence of filters means that the oscillations of the tag positions are not damped. This leads to the worst result of all the tests. It is possible to note that the easily reachable and visible points present low error values, while for the most critical points the system per‐

formance is worse, even reaching high error values (in the order of metres).


Table 4 presents the estimated and detected coordinates of the 16 points, specifying the error between them.


**Table 4.** Analysis of static *Test 3*

The average error of *Test 3* is 1.5709 m.

Like the other two tests, points 6 and 9 present largely incorrect values, because of the con‐ dition of the area in which they are located. The other values are in line with the estimated measurements.

#### **Test 4**


Table 5 presents the estimated and detected coordinates of the 16 points, specifying the error between them.


The first part of the paragraph presents the results obtained by dynamic tests, using a static filter (*Static Information Filter*), while the second part shows the same results using a dynam‐

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In order to cover the whole interested area, several proof paths are decided and measured in

The path is 28.8 m long: the first part is made up of an area with good coverage by sensors without obstacles, while in the second part the operator has to cross an area with numerous obstacles and metallic materials. Figure 14 shows the estimated path (Figure 14a) and the

detected path travelled by the operator, obtained using LEC software (Figure 14b).

Table 7 shows the detected and measured distances and the difference between them.

**Distance estimated [m] Distance travelled [m] Error [m] Error [%]**

28.8 31.22 2.421 8.408

**Figure 14.** a. Estimated path of dynamic *Test 1* b. Detected path of dynamic *Test 1*

ic filter (*Information Filter*), underlining the differences between them.

Four tests are performed, according to the following parameters:

*3.3.2.1. Dynamic tests using Static Information Filter*

**•** Velocity of tag: 2 m/sec at a constant height of 1.5 m.

**•** Filter: *Static Information Filter*;

**•** Update each four time slot;

**•** Threshold speed: 2 m/sec;

**Table 7.** Synthesis of dynamic *Test 1*

**•** Frequency: 37 Hz;

advance.

**Test 1**

**Table 5.** Analysis of static *Test 4*

The average error of *Test 4* is 1.4319 m.

In this case, the results are better than *Test 2* and *Test 3*, but the problems regarding the pres‐ ence of obstacles in the area to be monitored, noted during the other tests, remain.

From a comparison between the four static tests (Table 6), it is possible to note that the best algorithm in terms of the lowest average error between estimated and detected tag position is *Test 1* that uses a *Static Fixed Height Information Filter*.


**Table 6.** Comparison between the average errors of static tests

#### *3.3.2. Dynamic tests*

Dynamic tests are performed by applying a tag to an operator that goes around the laborato‐ ry following prefixed paths. The length of these paths, measured in advance, is compared with the real distance travelled by the operator. In this way, it is possible to see the precision of each known point and test the capacity of the system to reconstruct the trajectory.

The first part of the paragraph presents the results obtained by dynamic tests, using a static filter (*Static Information Filter*), while the second part shows the same results using a dynam‐ ic filter (*Information Filter*), underlining the differences between them.

#### *3.3.2.1. Dynamic tests using Static Information Filter*

Four tests are performed, according to the following parameters:


In order to cover the whole interested area, several proof paths are decided and measured in advance.

#### **Test 1**

**Point X [m] Y [m] X detected [m] Y detected [m] Error [m]** 5.826 11.113 6.1134 10.8474 0.3913 7.976 10.991 7.4098 11.1165 0.5798 11.389 11.207 11.4637 11.0003 0.2197 15.693 7.138 15.7641 7.0616 0.1043 18.627 7.138 18.7090 7.1121 0.0860 26.39 7.138 20.3066 4.3934 6.6738 26.39 11.204 26.5290 10.9065 0.3283 26.39 14.028 22.7547 9.2591 5.9963 26.39 16.6 27.3837 17.1635 1.1424 19.347 11.207 19.5325 10.7986 0.4484 8.17 7.113 5.5139 7.2697 2.6607 3.415 7.05 4.0360 7.3234 0.6786 3.399 15.739 3.4861 14.5849 1.1573 16.397 2.7481 14.9694 3.0857 1.4668 11.367 2.7481 11.8527 3.4522 0.8554 15.637 15.187 15.7347 15.1152 0.1212

In this case, the results are better than *Test 2* and *Test 3*, but the problems regarding the pres‐

From a comparison between the four static tests (Table 6), it is possible to note that the best algorithm in terms of the lowest average error between estimated and detected tag position

Dynamic tests are performed by applying a tag to an operator that goes around the laborato‐ ry following prefixed paths. The length of these paths, measured in advance, is compared with the real distance travelled by the operator. In this way, it is possible to see the precision

of each known point and test the capacity of the system to reconstruct the trajectory.

ence of obstacles in the area to be monitored, noted during the other tests, remain.

**Filter used Average error [m]** *Static Fixed Height Information Filter* 0.8236 Any filter applied 1.661 *Static Information Filter* 1.5709 *Information Filter* 1.4319

**Table 5.** Analysis of static *Test 4*

*3.3.2. Dynamic tests*

The average error of *Test 4* is 1.4319 m.

364 Radio Frequency Identification from System to Applications

is *Test 1* that uses a *Static Fixed Height Information Filter*.

**Table 6.** Comparison between the average errors of static tests

The path is 28.8 m long: the first part is made up of an area with good coverage by sensors without obstacles, while in the second part the operator has to cross an area with numerous obstacles and metallic materials. Figure 14 shows the estimated path (Figure 14a) and the detected path travelled by the operator, obtained using LEC software (Figure 14b).

**Figure 14.** a. Estimated path of dynamic *Test 1* b. Detected path of dynamic *Test 1*

Table 7 shows the detected and measured distances and the difference between them.


**Table 7.** Synthesis of dynamic *Test 1*

#### **Test 2**

The path is 30 m long and it travels around a metallic shelf in the centre of the laboratory. Figure 15 shows the estimated path (Figure 15a) and the detected path travelled by the oper‐ ator, obtained using LEC software (Figure 15b). As can be seen from Figure 15b, the bluesky line representing the path, presents some noises, due to the momentary loss of the signal.

**Figure 16.** a. Estimated path of dynamic *Test 3* b. Detected path of dynamic *Test 3*

**Figure 17.** a. Estimated path of dynamic *Test 4* b. Detected path of dynamic *Test 4*

**Table 10.** Synthesis of dynamic *Test 4*

The path is 11.5 m long. It is situated in a complex environment, characterized by the pres‐ ence of walls and several machines that strongly hinder correct signal reception by the sen‐ sors. Indeed, it is possible to observe the irregular trend that causes problems in the correct evaluation of the distance travelled. Figure 17 shows the estimated path (Figure 17a) and the

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detected path travelled by the operator, obtained using LEC software (Figure 17b).

Table 10 shows the detected and measured distances and the difference between them.

**Distance estimated [m] Distance travelled [m] Error [m] Error [%]**

11.5 14.42 2.9285 25.465

**Test 4**

**Figure 15.** a. Estimated path of dynamic *Test 2* b. Detected path of dynamic *Test 2*

Table 8 shows the detected and measured distances and the difference between them.


**Table 8.** Synthesis of dynamic *Test 2*

#### **Test 3**

The path is 23.5 m long: the first part is made up of an area with low coverage, because of the presence of walls, shelves and several metallic machines and objects. In the final part, the path is made up of an area surrounded by machineries and this makes the correct locali‐ zation of the tag difficult. Figure 16 shows the estimated path (Figure 16a) and the detected path travelled by the operator, obtained using LEC software (Figure 16b).

Table 9 shows the detected and measured distances and the difference between them.


**Table 9.** Synthesis of dynamic *Test 3*

**Figure 16.** a. Estimated path of dynamic *Test 3* b. Detected path of dynamic *Test 3*

#### **Test 4**

**Test 2**

366 Radio Frequency Identification from System to Applications

signal.

The path is 30 m long and it travels around a metallic shelf in the centre of the laboratory. Figure 15 shows the estimated path (Figure 15a) and the detected path travelled by the oper‐ ator, obtained using LEC software (Figure 15b). As can be seen from Figure 15b, the bluesky line representing the path, presents some noises, due to the momentary loss of the

**Figure 15.** a. Estimated path of dynamic *Test 2* b. Detected path of dynamic *Test 2*

**Table 8.** Synthesis of dynamic *Test 2*

**Table 9.** Synthesis of dynamic *Test 3*

**Test 3**

Table 8 shows the detected and measured distances and the difference between them.

**Distance estimated [m] Distance travelled [m] Error [m] Error [%]**

The path is 23.5 m long: the first part is made up of an area with low coverage, because of the presence of walls, shelves and several metallic machines and objects. In the final part, the path is made up of an area surrounded by machineries and this makes the correct locali‐ zation of the tag difficult. Figure 16 shows the estimated path (Figure 16a) and the detected

**Distance estimated [m] Distance travelled [m] Error [m] Error [%]**

23.5 24.02 0.5199 2.2125

Table 9 shows the detected and measured distances and the difference between them.

path travelled by the operator, obtained using LEC software (Figure 16b).

30 30.45 0.4594 1.5315

The path is 11.5 m long. It is situated in a complex environment, characterized by the pres‐ ence of walls and several machines that strongly hinder correct signal reception by the sen‐ sors. Indeed, it is possible to observe the irregular trend that causes problems in the correct evaluation of the distance travelled. Figure 17 shows the estimated path (Figure 17a) and the detected path travelled by the operator, obtained using LEC software (Figure 17b).

**Figure 17.** a. Estimated path of dynamic *Test 4* b. Detected path of dynamic *Test 4*



**Table 10.** Synthesis of dynamic *Test 4*

#### *3.3.2.2. Dynamic tests with Information Filter*

The authors decide to re-apply the same tests applying a dynamic filter, called *Information Filter,* to the algorithm, in order to compare the results with those obtained by using a static filter. If the sensors lose the signal, the static filter maintains the last detected position and updates it when a valid signal arrives. The dynamic filter, instead, stores the velocity and the direction of the tag moment all times and, in case of absence of valid signals, it assumes that the target continues to move in the same direction and at the same velocity as the last measurement. The use of a dynamic filter results in lower performance of operations for the reconstruction of trajectories, since the paths do not reflect the real tag movements.

The path travelled by an operator in the laboratory using a dynamic filter, presents more noise than that travelled using a static filter. It is possible to note some peaks along the path, due to loss of signal. Indeed the *Information Filter* allows the target to move along the three dimensions, but, if it is not seen for a period, the system assumes that it is moving along the

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In this case, the path is strongly modified at the point where the signal is lost. In particular, it is possible to observe the formation of straight lines that indicate that sensors were not able to detect the tag presence for some seconds. In this way, the last trajectory is main‐ tained, but it does not reflect the real path travelled by the target. Figure 19 shows the com‐ parison between the maps obtained using LEC software, in the case of static (Figure 19a) and dynamic filter (Figure 19b). The arrows show straight lines formed because of the loss

(a) (b)

In this case, the errors in the traceability of the path are less evident than in the last case, but it is possible to note that the line appears more indented. This is an indication of more noises during localization. Moreover, in the final part, the trace overlaps with a wall, underlining the limits of the localization with the dynamic filter. Figure 20 shows the comparison be‐ tween the maps obtained using LEC software, in the case of static (Figure 20a) and dynamic filter (Figure 20b). The arrows show the main differences between the paths travelled using

(a) (b)

 Figure 6. Path traced with dynamic filtering algorithm (on the left) and static filtering algorithm (on the right)

same direction and at the same velocity.

of signal by the sensors, unlike the case of a static filter.

Figure 19. a. Path travelled using static filter b. Path travelled using dynamic filter

**Figure 19.** a. Path travelled using static filter b. Path travelled using dynamic filter

Figure 20. a. Path travelled using static filter b. Path travelled using dynamic filter

**Test 2**

**Test 3**

a static and a dynamic filter.

The tests are performed according to the same parameters as the dynamic tests with a static filter:


The paths are the same as the dynamic tests with static filter.

#### **Test 1**

The application of a dynamic filter does not heavily modify the results, except for the central stretch and the last part of the path, since it is made up of metallic materials. Figure 18 shows the comparison between the maps obtained using LEC software, in the case of static (Figure 18a) and dynamic filter (Figure 18b). The arrows show the main differences between the paths travelled using a static and a dynamic filter.

**Figure 18.** a. Path travelled using static filter b. Path travelled using dynamic filter

The path travelled by an operator in the laboratory using a dynamic filter, presents more noise than that travelled using a static filter. It is possible to note some peaks along the path, due to loss of signal. Indeed the *Information Filter* allows the target to move along the three dimensions, but, if it is not seen for a period, the system assumes that it is moving along the same direction and at the same velocity.

#### **Test 2**

*3.3.2.2. Dynamic tests with Information Filter*

368 Radio Frequency Identification from System to Applications

filter:

**Test 1**

**•** Filter: *Information Filter*;

**•** Frequency: 37 Hz;

**•** Update each four time slot;

**•** Threshold speed: 2 m/sec;

**•** Velocity of tag: 2 m/sec at a constant height of 1.5m.

the paths travelled using a static and a dynamic filter.

**Figure 18.** a. Path travelled using static filter b. Path travelled using dynamic filter

The paths are the same as the dynamic tests with static filter.

The authors decide to re-apply the same tests applying a dynamic filter, called *Information Filter,* to the algorithm, in order to compare the results with those obtained by using a static filter. If the sensors lose the signal, the static filter maintains the last detected position and updates it when a valid signal arrives. The dynamic filter, instead, stores the velocity and the direction of the tag moment all times and, in case of absence of valid signals, it assumes that the target continues to move in the same direction and at the same velocity as the last measurement. The use of a dynamic filter results in lower performance of operations for the

The tests are performed according to the same parameters as the dynamic tests with a static

The application of a dynamic filter does not heavily modify the results, except for the central stretch and the last part of the path, since it is made up of metallic materials. Figure 18 shows the comparison between the maps obtained using LEC software, in the case of static (Figure 18a) and dynamic filter (Figure 18b). The arrows show the main differences between

reconstruction of trajectories, since the paths do not reflect the real tag movements.

In this case, the path is strongly modified at the point where the signal is lost. In particular, it is possible to observe the formation of straight lines that indicate that sensors were not able to detect the tag presence for some seconds. In this way, the last trajectory is main‐ tained, but it does not reflect the real path travelled by the target. Figure 19 shows the com‐ parison between the maps obtained using LEC software, in the case of static (Figure 19a) and dynamic filter (Figure 19b). The arrows show straight lines formed because of the loss of signal by the sensors, unlike the case of a static filter. Figure 6. Path traced with dynamic filtering algorithm (on the left) and static filtering algorithm (on the right)

**Figure 19.** a. Path travelled using static filter b. Path travelled using dynamic filter

Figure 19. a. Path travelled using static filter b. Path travelled using dynamic filter

Figure 20. a. Path travelled using static filter b. Path travelled using dynamic filter

#### **Test 3**

In this case, the errors in the traceability of the path are less evident than in the last case, but it is possible to note that the line appears more indented. This is an indication of more noises during localization. Moreover, in the final part, the trace overlaps with a wall, underlining the limits of the localization with the dynamic filter. Figure 20 shows the comparison be‐ tween the maps obtained using LEC software, in the case of static (Figure 20a) and dynamic filter (Figure 20b). The arrows show the main differences between the paths travelled using a static and a dynamic filter.

(a) (b)

Figure 19. a. Path travelled using static filter b. Path travelled using dynamic filter

**3.4. RFID technology applied to packaging system**

packaging are better service and lower costs.

RFID technology is introduced in the packaging sector due to the logistics advantages re‐ garding the utilization of automatic identification systems. This introduction mainly focuses on the secondary and tertiary packaging levels because the utilization in the item level (product identification) has been difficult to justify in economic terms [30]. Specifically, 250-300 millions of tags were used in 2006 in the tertiary level [31]. Furthermore, Thoroe et al. [32] have predicted that in 2016 there will be 450 times more RFID tags in use than today. Therefore, a rapid increase in RFID tag consumption is expected in the packaging sector.

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Technological developments in recent years, along with a reduction in tag price and emerg‐ ing standards have facilitated trials and rollouts of RFID technology in packaging. A study conducted by IDTechEx Limited [33] stated that the main benefits of RFID technology in

Packaging incorporating RFID technology is usually referred to as *smart packaging* (called also *active* or *intelligent packaging*) and it is commonly used to describe packaging with different types of value-adding technologies, for example placing in the package a smart label or tag. The term smart packaging was used by Yam [34] in 1999 to emphasize the role of packaging as an intelligent messenger or an information link. According to the Smart Packaging Journal [35], smart packaging is described as *packaging that employs features of high added value that en‐ hances the functionality of the product* and its core is responsive features. These high-value fea‐ tures have a variety of characteristics, but are mainly made up of mechanical or electronic technology features such as mechanical medicines, dispenser of packaging tagged with elec‐ tronic devices like RFID technology. Smart packaging is often used to refer to electronic re‐ sponsive features where data is electronically sensed on the package from a distance, using an automatic identification system as the RFID technology. Schilthuizen [36] pointed out that

identification and sensor technology enable intelligent functions in packaging.

ging different packaging levels has on the retail supply chain processes.

Usually packages – and the products contained within them – are traced with systems ob‐ tained through asynchronous fulfilment of doorways by materials. In such cases, the track‐ ing is totally manual, executed by operators. These manual activities could be eliminated or replaced by an automated identification activity, using an RFID system. The application of RFID to packaging allows more frequent and automated identification of packages (e.g. pal‐ lets, cases, and items) increasing the accuracy of the system, reducing the labour and time needed to perform the identification of packages and enabling near real-time visibility, which in turn facilities the coordination of activities within and between processes. The costs of RFID technology in packaging and potential benefits will vary, according to the packag‐ ing level that is tagged. Figure 22 (modified version of [25]) illustrates the influence that tag‐

(a) (b)

 Figure 6. Path traced with dynamic filtering algorithm (on the left) and static filtering algorithm (on the right)

**Figure 20.** a. Path travelled using static filter b. Path travelled using dynamic filter

Figure 20. a. Path travelled using static filter b. Path travelled using dynamic filter

#### **Test 4**

In this case, the errors in the traceability of the path are evident, because of the critical envi‐ ronment in which the path is travelled. In the middle of the path the signal is lost and found again only in the proximity of the final part of the path. This leads to the creation of a straight line that does not reflect the real movement of the tag. Figure 21 shows the compari‐ son between the maps obtained using LEC software, in the case of static (Figure 21a) and dynamic filter (Figure 21b). The arrows show the main differences between the paths travel‐ led using a static and a dynamic filter.

**Figure 21.** a. Path travelled using static filter b. Path travelled using dynamic filter

The algorithm using a static filter provides better results than that using the dynamic filter. A comparisons between the two algorithms show that if the sensors lose the tag signals for a period, the system assumes that the tags continue to move according to the last velocity val‐ ue and along the last direction of movement. The greater the moment of no-detection of tag's position, the higher the inaccuracy of the system, causing a distortion of the path.

#### **3.4. RFID technology applied to packaging system**

 Figure 6. Path traced with dynamic filtering algorithm (on the left) and static filtering algorithm (on the right)

(a) (b)

(a) (b)

In this case, the errors in the traceability of the path are evident, because of the critical envi‐ ronment in which the path is travelled. In the middle of the path the signal is lost and found again only in the proximity of the final part of the path. This leads to the creation of a straight line that does not reflect the real movement of the tag. Figure 21 shows the compari‐ son between the maps obtained using LEC software, in the case of static (Figure 21a) and dynamic filter (Figure 21b). The arrows show the main differences between the paths travel‐

The algorithm using a static filter provides better results than that using the dynamic filter. A comparisons between the two algorithms show that if the sensors lose the tag signals for a period, the system assumes that the tags continue to move according to the last velocity val‐ ue and along the last direction of movement. The greater the moment of no-detection of tag's position, the higher the inaccuracy of the system, causing a distortion of the path.

Figure 19. a. Path travelled using static filter b. Path travelled using dynamic filter

370 Radio Frequency Identification from System to Applications

Figure 20. a. Path travelled using static filter b. Path travelled using dynamic filter **Figure 20.** a. Path travelled using static filter b. Path travelled using dynamic filter

**Figure 21.** a. Path travelled using static filter b. Path travelled using dynamic filter

led using a static and a dynamic filter.

**Test 4**

RFID technology is introduced in the packaging sector due to the logistics advantages re‐ garding the utilization of automatic identification systems. This introduction mainly focuses on the secondary and tertiary packaging levels because the utilization in the item level (product identification) has been difficult to justify in economic terms [30]. Specifically, 250-300 millions of tags were used in 2006 in the tertiary level [31]. Furthermore, Thoroe et al. [32] have predicted that in 2016 there will be 450 times more RFID tags in use than today. Therefore, a rapid increase in RFID tag consumption is expected in the packaging sector.

Technological developments in recent years, along with a reduction in tag price and emerg‐ ing standards have facilitated trials and rollouts of RFID technology in packaging. A study conducted by IDTechEx Limited [33] stated that the main benefits of RFID technology in packaging are better service and lower costs.

Packaging incorporating RFID technology is usually referred to as *smart packaging* (called also *active* or *intelligent packaging*) and it is commonly used to describe packaging with different types of value-adding technologies, for example placing in the package a smart label or tag. The term smart packaging was used by Yam [34] in 1999 to emphasize the role of packaging as an intelligent messenger or an information link. According to the Smart Packaging Journal [35], smart packaging is described as *packaging that employs features of high added value that en‐ hances the functionality of the product* and its core is responsive features. These high-value fea‐ tures have a variety of characteristics, but are mainly made up of mechanical or electronic technology features such as mechanical medicines, dispenser of packaging tagged with elec‐ tronic devices like RFID technology. Smart packaging is often used to refer to electronic re‐ sponsive features where data is electronically sensed on the package from a distance, using an automatic identification system as the RFID technology. Schilthuizen [36] pointed out that identification and sensor technology enable intelligent functions in packaging.

Usually packages – and the products contained within them – are traced with systems ob‐ tained through asynchronous fulfilment of doorways by materials. In such cases, the track‐ ing is totally manual, executed by operators. These manual activities could be eliminated or replaced by an automated identification activity, using an RFID system. The application of RFID to packaging allows more frequent and automated identification of packages (e.g. pal‐ lets, cases, and items) increasing the accuracy of the system, reducing the labour and time needed to perform the identification of packages and enabling near real-time visibility, which in turn facilities the coordination of activities within and between processes. The costs of RFID technology in packaging and potential benefits will vary, according to the packag‐ ing level that is tagged. Figure 22 (modified version of [25]) illustrates the influence that tag‐ ging different packaging levels has on the retail supply chain processes.

**Figure 22.** The influence of tagging different packaging levels along the supply chain (modified version of [25])

**Figure 23.** Traceability of a package with RFID-UWB system (Spaghetti Chart)

wall. This causes the incomplete coverage of the monitored area.

tem is perfect for the traceability of goods.

package traceability.

**3.5. Results and discussion**

The traditional approach provides the well known *Spaghetti Chart* (manually realized). In addition, the data is approximate and does not provide precise values. In order to overcome the difficulty in analysing data from a traditional tracking method, Real Time Location Sys‐

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The framework on RFID and packaging shows the importance of tracing packages since sev‐ eral benefits can be achieved (e.g. reduction of costs and time, increase of efficiency and ef‐ fectiveness, accuracy of the activities along the supply chain, security of the products, etc.). The RFID-UWB system presented in the chapter is perfectly aligned with the problem of

The results obtained during the static tests show that the average error between the estimat‐ ed and the detected measurements is approximately 1 m. However, it is important to con‐ sider the non-optimal installation of the sensors. In fact, the most suitable arrangement to obtain the maximum coverage of the area is obtained by placing the sensors at the corners of the area to be monitored. Because of the presence of obstacles and metallic objects, the au‐ thors have had to opt for an alternative solution, placing each sensor in the middle of each

Despite this limitation, the authors have chosen to include in the tests some points located outside the optimal coverage area. In these cases, the obtained accuracy is much lower than that obtained by points located where the coverage is maximum. For example, in some cas‐ es, there are errors of several meters, not compatible with the project needs and that affect the estimation of the average error that increases greatly. For this reason, these points are

As can be noted in Figure 22, RFID tags on tertiary packaging may be used from the filling to the storing process. Furthermore, the tags on tertiary packaging may be used from the shipping process of the distribution centre to the receiving and shipping process of the retail outlet. RFID tags on secondary packaging could be used further downstream in the supply chain than tagged tertiary packaging, i.e. from the filling process and all the way to the re‐ plenishing process. Irrespective of the activities within the replenishment processes, tagging of primary packaging may be used in the whole supply chain, from the point of filling by the manufacturer to the point of sale in the retail outlet. Tagging of primary and secondary packaging could also provide opportunities beyond the point of sale in retail outlets e.g. re‐ cycling, reusing, and post-sales service and support. Although tagging on the primary pack‐ aging level will bring about the greatest level of benefits for the retail supply chain, tagging on secondary and tertiary packaging levels could provide valuable benefits for the supply chain. The model presented in Figure 22 indicates that a manufacturer who applies the tags to packaging can gain direct benefits from primary and secondary packaging tagging. Ac‐ cording to [25], the average time to pick an order decreases by roughly 25% when RFID technology is used in secondary packaging. This means that the workforce conducting the picking activity, which is the core and the most labour-intensive activity in distribution cen‐ tres could be reduced by approximately 25%. Hellström [25] also stated that the ability to automatically generate orders by capturing the inventory levels through tagging of primary packaging could reduce out-of-stock situations by approximately 50%.

Figure 23 shows the traceability of a primary package patterns within a manufacturing com‐ pany (in particular in an assembly station) using the RFID-UWB system.

**Figure 23.** Traceability of a package with RFID-UWB system (Spaghetti Chart)

The traditional approach provides the well known *Spaghetti Chart* (manually realized). In addition, the data is approximate and does not provide precise values. In order to overcome the difficulty in analysing data from a traditional tracking method, Real Time Location Sys‐ tem is perfect for the traceability of goods.

The framework on RFID and packaging shows the importance of tracing packages since sev‐ eral benefits can be achieved (e.g. reduction of costs and time, increase of efficiency and ef‐ fectiveness, accuracy of the activities along the supply chain, security of the products, etc.). The RFID-UWB system presented in the chapter is perfectly aligned with the problem of package traceability.

#### **3.5. Results and discussion**

**Manufacturers Carriers Wholesalers Carriers Retailers**

Receiving Warehousing Picking Shipping

Shipping Filling Warehousing

**Figure 22.** The influence of tagging different packaging levels along the supply chain (modified version of [25])

As can be noted in Figure 22, RFID tags on tertiary packaging may be used from the filling to the storing process. Furthermore, the tags on tertiary packaging may be used from the shipping process of the distribution centre to the receiving and shipping process of the retail outlet. RFID tags on secondary packaging could be used further downstream in the supply chain than tagged tertiary packaging, i.e. from the filling process and all the way to the re‐ plenishing process. Irrespective of the activities within the replenishment processes, tagging of primary packaging may be used in the whole supply chain, from the point of filling by the manufacturer to the point of sale in the retail outlet. Tagging of primary and secondary packaging could also provide opportunities beyond the point of sale in retail outlets e.g. re‐ cycling, reusing, and post-sales service and support. Although tagging on the primary pack‐ aging level will bring about the greatest level of benefits for the retail supply chain, tagging on secondary and tertiary packaging levels could provide valuable benefits for the supply chain. The model presented in Figure 22 indicates that a manufacturer who applies the tags to packaging can gain direct benefits from primary and secondary packaging tagging. Ac‐ cording to [25], the average time to pick an order decreases by roughly 25% when RFID technology is used in secondary packaging. This means that the workforce conducting the picking activity, which is the core and the most labour-intensive activity in distribution cen‐ tres could be reduced by approximately 25%. Hellström [25] also stated that the ability to automatically generate orders by capturing the inventory levels through tagging of primary

Reuse/ Recycling/ Disposal

Centres Transport Retail outlets End

consumer

Point of safe

Replenishing Receiving/

Manufacturing Transport Distribution

**RFID tags on tertiary packaging (pallet level) RFID tags on secondary packaging (case level) RFID tags on primary packaging (item level)**

372 Radio Frequency Identification from System to Applications

Product filling point

> From retail outlets

packaging could reduce out-of-stock situations by approximately 50%.

pany (in particular in an assembly station) using the RFID-UWB system.

Figure 23 shows the traceability of a primary package patterns within a manufacturing com‐

The results obtained during the static tests show that the average error between the estimat‐ ed and the detected measurements is approximately 1 m. However, it is important to con‐ sider the non-optimal installation of the sensors. In fact, the most suitable arrangement to obtain the maximum coverage of the area is obtained by placing the sensors at the corners of the area to be monitored. Because of the presence of obstacles and metallic objects, the au‐ thors have had to opt for an alternative solution, placing each sensor in the middle of each wall. This causes the incomplete coverage of the monitored area.

Despite this limitation, the authors have chosen to include in the tests some points located outside the optimal coverage area. In these cases, the obtained accuracy is much lower than that obtained by points located where the coverage is maximum. For example, in some cas‐ es, there are errors of several meters, not compatible with the project needs and that affect the estimation of the average error that increases greatly. For this reason, these points are eliminated in the computing of the average error estimation, obtaining a considerable im‐ provement in the accuracy, reaching an average error of 40 cm.

such as no line-of-sight requirements, high accuracy and resolution and the possibility to trace multiple resources in real-time. Furthermore, RFID-UWB sensors are cheaper, and this

Manufacturing Logistics and Packaging Management Using RFID

http://dx.doi.org/10.5772/53890

375

In order to trace the position and to map the movements of targets (e.g. people, materials, products, vehicles, information), the authors have developed an experimental IPS system based on RFID-UWB technology in the Laboratory of Manufacturing System of Bologna University which, thanks to the presence of walls, machineries and metal objects, can repre‐ sent a real industrial application. The system is made up of active tags – positioned on fork‐ lifts, packages, or operators –, sensors that receive the signal from tags, and a software platform that collects data in order to present, analyse and communicate information to the final customer. The tags, which must be positioned around the tested areas, transmit short pulses to the sensors, organized in rectangular cells. Each cell is characterized by a main sen‐ sor (*master*) that coordinates the activities of the other sensors (*slave*) and communicates to the tags the detected position within the cell. The software platform carries out the position‐

ing calculations based on information by the sensors and then analyses the results.

The experimental research consists of several tests, static and dynamic. The results present useful conclusions in terms of system performance, accuracy, and measurement precision.

The static tests give good results in terms of average error (approximately 40 cm) between the estimated and detected position of all considered points. The dynamic tests are per‐ formed using filters that regulate the behaviour of tags. The filters can be static or dynamic. The tests performed by applying a static filter produce better results compared with dynam‐ ic filter. If the sensors lose the signal and the filter is dynamic, the system continues to see the tag moving along the same direction and at the same velocity as the last measurement. On the other hand, if the filter is static, the system assumes that the position of tag is the same as the last measured. In conclusion, systems using static filter provide more accurate results (with an average error between the estimated and detected real distance travelled of

RFID technology can be also applied to packaging. Although the use of RFID technology in packaging is still limited, more and more companies are recognizing the importance of trac‐ ing packaging products moving within indoor environments. During recent decades, the importance of packaging and its functions is been increasing. Packaging is considered an in‐ tegral element of logistics systems and its main function is to protect and preserve products. More often companies have to transport and distribute particular goods (e.g. dangerous or explosive products) or expensive products, such as some kinds of medicines. Since compa‐ nies need to reduce thefts, increase security, and reduce costs and time spent on the tracea‐

Rapid advances in factory automation in general and packaging operations in particular have posed a challenge for engineering and technology programs for educating a qualified workforce to design, operate and maintain cutting edge techniques such as RFID systems

makes the RFID-UWB positioning system a cost-effective solution.

5%) than systems using the dynamic filter.

bility of products, they are starting to use RFID in packaging.

[37]. The system proposed by the authors tries to play this challenge.

Regarding dynamic tests, the problems connected with the layout of the area are the same as the static ones. The authors have set the tests to simulate paths all around the area. Several critical points cannot be seen by the sensors. In particular, in some areas, the tag is seen only by one or two sensors, which results in inaccuracies in the traceability of the path travelled by the target.

Unlike the static tests, during the dynamic tests, it is necessary to control the typology of fil‐ ters used. The results show that the best performance is obtained using a static (*Static Infor‐ mation Filter*), rather than a dynamic (*Information Filter*) filter, with an error of 5% between the estimated and the real distance travelled. If the sensors lose the signal and the filter is dynamic, the system continues to see the tag moving along the same direction and at the same velocity as the previous measurement. On the contrary, if the sensors do not see the signal and the filter is static, the system assumes that the position of tag is the same as the last measured. In conclusion, systems using dynamic filters provide less accurate results than systems using static filters.

In order to improve the performance of the system, several changes could be made:

