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THE ROLE OF IOT DATA AGGREGATORS FOR OPTIMISING OBJECT
TRACKING AND KPI MONITORING
Robert Idlbek
Faculty of Tourism and Rural Development, Croatia
Email: ridlbek@ftrr.hr
Verica Budimir
Faculty of Tourism and Rural Development, Croatia
Email: vbudimir@ftrr.hr
Abstract
The Internet of Things (IoT) is an innovative technology that has completely
transformed how different devices communicate. This includes sensors, actuators,
GPS trackers, and other intelligent equipment. Among its many applications, one of
the most important is its role in object tracking and monitoring Key Performance
indicators (KPI). These functions are particularly crucial for logistics, manufacturing,
agriculture, and retail industries.
The main objective of this paper is to explore the significance of IoT data aggregators
in optimising these business processes. IoT data aggregators have a vital role to play
as they gather, process, and analyse data from multiple IoT devices. This
comprehensive approach allows a thorough understanding of the monitored objects
and their performance. Moreover, the paper investigates how software designed for
data aggregation can enhance the accuracy and efficiency of object tracking. This
improvement facilitates real-time tracking of objects indoors and outdoors, analysis
of past movements and events, and even predictive maintenance.
Additionally, the paper examines how data aggregators contribute to improved KPI
monitoring by providing real-time performance metrics. These metrics enable
proactive decision-making and enhance operational efficiency. However, addressing
some technical challenges associated with object monitoring and data aggregation is
essential, such as interoperability and vendor-free technology.
Keywords: IoT data aggregators, object tracking, Key Performance Indicator, KPI
monitoring, operational efficiency.
1. INTRODUCTION
In today's academic realm, it becomes apparent that further investigation is necessary
to delve into the significance of IoT data aggregators in optimising object tracking
and Key Performance Indicator (KPI) monitoring processes. While existing studies
explore the utilisation of IoT technology for these intentions, they overlook the pivotal
role data aggregators play in collecting, processing, and analysing the vast amount of
data generated by IoT devices (mainly sensors and actuators). The current research
primarily centres around IoT devices and implementing data analytics techniques for
object tracking. However, limited attention has been given to the importance of data
aggregators in effectively managing data flow between IoT devices and analytics
systems.
This paper seeks to explore the implications of using software designed specifically
for data aggregation to strengthen the accuracy and effectiveness of object tracking.
This advancement aims to provide the information required for business analytics and
reporting. The data analysed can provide insights that can shape strategic business
decisions. The field of the article is Information Technology and Economics,
explicitly focusing on the Internet of Things (IoT) and Data Analytics.
The field and topic of this work are important because IoT and Data Analytics, along
with the ever-present Artificial Intelligence, are key drivers of today's digital
transformation. These areas are necessary for all economic industries.
Furthermore, IoT devices generate considerable data that can be analysed in real-time.
That is particularly important in healthcare, manufacturing and logistics industries,
where real-time data can save lives, prevent downtime and optimise supply chains.
When the field of data analytics (Data Analytics) is added to this, which enables the
prediction of future trends and behaviour, it allows a much simpler transition from a
reactive practice to a proactive one. By analysing previous data and using different
prediction algorithms (predictive algorithms), it is possible to change the way of doing
business and making decisions significantly. Suppose we add the ability to monitor
the company's performance by defining and tracking KPIs. In that case, it is possible
to additionally automate routine tasks and have information about business processes
practically instantly. There are infinite examples, and this paper focuses on a sample
from the logistics field, i.e., the analysis of vehicle driving quality, which is impossible
using classic GPS devices.
In conclusion, IoT industry monitoring, data analytics and the ability to monitor
economic indicators are essential and crucial for the growth and sustainability of
companies and economies in the 21st century. Digital transformation is currently
based on technical platforms that are not mature and sufficiently interoperable. Not to
mention that the challenges of ensuring data security in the IoT world are a broad and
exciting topic of their own.
The research gap in this article is the technical challenges associated with object
monitoring and data aggregation in the context of IoT. By reviewing the current
literature, we can conclude that some of the main concerns and research gaps
nowadays regarding this topic are the following:
• What scalable solutions can be developed to handle the increasing volume of
data generated by IoT devices?
• What strategies can be developed to enable real-time analysis of the massive
data generated by IoT devices?
• How can interoperability among IoT devices and platforms be improved to
facilitate seamless data integration and analysis?
• How can businesses overcome the challenge of vendor lock-in in IoT
solutions?
• What methods can be employed to improve the quality of data IoT devices
collect?
• How can the security and privacy concerns associated with IoT be effectively
addressed to prevent data breaches and privacy violations?
To sum up, the main question can be asked: which technology platforms for data
aggregation will integrate all the necessary functionalities required for further digital
transformation?
This paper provides an overview of more recent research on the mentioned topic and
a sample from the field of outdoor object tracking.
2. METHODS
The primary objective of this paper is to showcase how a data aggregator can be
utilised as a foundation for business reporting and monitoring organisational goals
using key performance indicators (KPIs). Due to the paper's length, we will not delve
into the method of making business decisions based on analysed and presented data,
as it should be self-evident. Instead, we will explain the data collection, aggregation,
and analysis process.
Firstly, we explored the advances in indoor and outdoor object tracking technologies,
as well as provide a review of the most important literature in the field.
We used state-of-the-art GPS/Glonass/Galileo 4G tracker devices and 3-axis
accelerometers in eight vehicles to achieve this purpose. These vehicles were tracked
via satellite (GNSS) for a one-week duration. The collected data, amounting to
approximately 25 Mb, was extensively analysed. This analysis led to the creation a
highly analytical report for the management. To ensure the accuracy of the data, we
utilised the new generation devices, with Wialon services (Gurtam) serving as the
analysis tool. The core concept of this paper centres around demonstrating the
potential of consolidating various types of data into a unified source, commonly
referred to as "data aggregators". This accumulation of data allows for simplified
representation and facilitates efficient analysis.
Furthermore, we mention the possibility of conducting further data analysis using
artificial intelligence systems and Neural Language Processing. It offers the potential
for gaining additional information if a need arises and better insight into what is of
great value for making decisions. It also leads to a better understanding of business
data and a better definition of Key Performance Indicators.
3. RESULTS
3.1. Literature Review
Numerous research papers discuss various aspects of GPS outdoor and indoor
positioning and tracking, as well as technological aspects of data aggregation and its
impact on business processes. Also, numerous papers discuss their usability in real-
world scenarios. Some essential papers and their references are mentioned in this
chapter, and some papers are in the following chapters of the research results.
Bakhru 2005 explores different outdoor and indoor tracking techniques, including
GPS modifications for indoor applications and additional sensors like IMU and
MEMS. Hutabarat (2016) combines RFID and GPS for human tracking in both indoor
and outdoor areas, achieving high accuracy. Gerdisen (2014) focuses on GPS-based
human tracking in closed areas, developing an Android application with an error
estimation of around 4 meters, which is more than expected. These papers provide
insights into using GPS for outdoor tracking, including vehicle and human tracking,
and highlight the potential of combining GPS with other technologies for improved
accuracy and coverage.
Numerous papers collectively provide insights into IoT data aggregators. Uddin
(2017) proposes a dynamic clustering and data-gathering scheme for IoT in
agriculture, utilising an Unmanned Aerial Vehicle (UAV) to assist ground IoT devices
in forming clusters and establishing a reliable communication backbone. Saleem
(2020) highlights the importance of data analytics in IoT applications, emphasising its
role in extracting meaningful insights for intelligent decision-making and
performance optimisation. Arora (2017) presents a multi-representation-based data
processing architecture for IoT applications, storing data in multiple representations
to cater to diverse application demands and enabling real-time analytics. Farrell
(2022) in their work introduces the IoT2SD framework, which incorporates an
Intrusion Detection System (IDS) to structure unstructured IoT MQTT message data
for data analytics purposes. This paper also covers the importance of data security in
the IoT world. In addition to others, these papers discuss various approaches and
frameworks for aggregating and processing IoT data. They highlight the benefits of
efficient data analytics and the potential applications in different domains and
industries.
When we seek to provide insights into key performance indicators for the Internet of
Things (IoT), there are numerous papers in this domain as well. Malier (2016)
emphasises the importance of digital technologies, such as FD-SOI, in enabling IoT
devices to combine edge computing capabilities with RF or sensor functionalities.
Lai-wu (2011) discusses key technologies based on RFID for achieving intellectual
identification, location tracking, and management in IoT applications. Babu (2017)
focuses on the performance analysis of data protocols in the network tier of IoT,
reviewing protocols like MQTT, MQTT-SN, AMQP, CoAP, XMPP, and DDS and
comparing them based on metrics such as network packet loss rate, message size,
bandwidth consumption, and latency. Also, numerous papers point out how to prepare
IoT-based Big Data for analysis (Kumar et al., 2022).In summary, these papers
collectively highlight the significance of digital technologies, RFID-based
technologies, and data protocols in ensuring the efficient performance of IoT systems
with a straightforward key point in mind - how to create a solid standpoint to measure
their performance.
3.2. Current Advances in Object Tracking Technologies
In today's increasingly connected world, the ability to track objects has become a
crucial aspect of our daily lives. Whether monitoring the movement of goods in a
warehouse or keeping track of personal belongings, indoor and outdoor tracking
systems have revolutionised how businesses manage and locate objects. This report
explores the nuances of indoor and outdoor tracking, shedding light on the various
technologies and methods involved. Vital factors contributing to the successful and
confident tracking of objects are understanding the differences between indoor and
outdoor environments, harnessing the power of GPS and geostationary satellite
systems, leveraging Bluetooth, UWB and IoT technologies, and utilising data
aggregators. By delving into the intricacies of these factors, we can gain a
comprehensive understanding of the advancements in object tracking and their
implications for various industries.
3.2.1. Advances in Outdoor Object Positioning
Global Navigation Satellite System (GNSS) is the central outdoor object positioning
and tracking technology. The system is formed from geostationary satellites and
required receivers. Most new satellite monitoring devices (GNSS trackers) can
simultaneously use more than one geostationary system. This allows location
interpolation, leading to slightly more accurate results than relying only on one
system. Geopositioning accuracy can vary between 2 to 10 meters, even in such cases.
This variation is dependent on the location and various additional factors. Real-time
Kinematic Positioning (RTK) corrections have shown the potential to achieve
accuracy within a few centimetres (Kumar et al., 2021). However, that technology
does not apply to devices in motion, and it is limited to stationary devices used for
position collection (such as GMSS devices used for this research).
The issue of precise positioning is widely recognised and extensively discussed in a
series of research articles. Numerous problems that are associated with geopositioning
are primarily problems of a physical and technical nature. These problems encompass
satellite and receiver clock errors, multipath errors, ionospheric delay, tropospheric
delay, and GPS ephemeris errors (Kumar et al., 2021). Research on the possible use
of a GPS receiver as an acceleration sensor has been conducted by, for example,
Sokolova, Borio, Forssell, & Lachapelle (2010). The results seem to confirm that the
mathematical model for that is satisfactory.
When it comes to outdoor tracking, the challenges become more complex due to
factors such as (1) varying lighting conditions, (2) occlusions, and (3) unpredictable
object motion. To overcome that, researchers have explored technologies such as
Inertial Measurement Units (IMUs) and sensor fusion techniques. GPS and other
satellite GNSS systems provide accurate location information by utilising signals
from satellites, but its accuracy can be compromised in urban environments with tall
buildings and signal obstructions. IMUs, on the other hand, use sensors such as
accelerometers and gyroscopes to measure the object's motion and orientation.
Combining data from multiple sensors using sensor fusion techniques can improve
the accuracy and robustness of outdoor tracking systems (Huang et al., 2010).
Information regarding the acceleration of the monitored object cannot be solely
derived from the current position data, as stated before. To attain this information,
GPS tracker devices need accelerometers, which measure the G-forces acting upon
the object along the X, Y, and Z axes. Incorporating a triaxial acceleration sensor and
gyroscope enables the creation of comprehensive reports on eco-driving in
automobiles or trucks. That includes not just the velocity attained from the satellite
system but also measurements of the driver's level of aggression in acceleration,
braking, and changing direction throughout the journey. A detailed analysis of a
particular driver's behaviour becomes achievable by considering the anticipated G-
forces for a typical passenger vehicle (which surpasses those observed in trucks or
buses). This methodology significantly decreases fuel consumption and minimises the
wear and tear on various vehicle components such as tires, suspensions, etc. The
primary aim of all these efforts revolves around optimising fleet management costs.
The application of IMU technology is viable in almost every aspect of object
monitoring, irrespective of whether the object is indoors or outdoors.
If the precise location is not required for outdoor tracking, the unit does not need a
GPS module at all. It can acquire its approximate position based on LBS data
(Location-Based Services, Cell-ID) and information on what GSM repeater unit is
currently connected. LBS location can be informative when there is no GNSS signal,
devices must operate in low battery mode, or when the tracked object is inside a
building where a GPS signal cannot pass through. LBS position is more accurate in
the urban and more populated areas because there are more GSM repeaters. However,
it can still show a false distance of about a few hundred meters and even ten kilometres
in unpopulated areas (Samama, 2019).
Contrary to the widely held belief that GNSS technology has already peaked, it is
undeniably constantly advancing and undergoing remarkable enhancements.
According to Rizos (2005), significant improvements are anticipated from various
perspectives soon from the (1) communication point of view, (2) instrumentation and
techniques, (3) hardware and (4) software point of view. Those enhancements will
additionally advance current technology for outdoor tracking.
3.2.2. Advances in Indoor Object Positioning
The precise tracking of an object's location within enclosed spaces such as buildings,
underground parking lots, and airports is not feasible with GNSS technology. Multiple
reasons exist for this limitation, including a significant decrease in signal quality and
quantity from geostationary satellites within enclosed spaces. Additionally, various
materials obstruct or reflect microwave signals, negatively impacting accuracy.
Interferences also occur within the 1100 to 1600 MHz radiation spectrum, the range
most GNSS receivers operate.
The GNSS positioning system offers a notable advantage because users do not need
to invest in supplementary equipment installed locally. The sole requirement is
possessing a GNSS receiver (tracker device). However, when it comes to indoor
object positioning, aside from deploying a transmitter on the targeted object, one must
also establish an infrastructure that facilitates tracking within enclosed spaces. That
entails incurring extra costs and maintaining equipment such as WiFi hotspots, RFID
antenna systems, BLE or UWB receivers, etc.
As a result of these factors, the indoor positioning system (IPS) relies on distinct
technological aspects compared to GNSS. Primarily used for business purposes, IPS
is widely adopted for navigation in commercial, military, and civilian domains. Retail
is one field where IPS finds applications, allowing tracking customers within a store
and enhancing understanding of their behaviour. Leveraging data on customer
movement can lead to improved product placement, optimised store layouts, and
personalised marketing messages. IPS facilitates tracking medical equipment and
patients within a hospital setting. That helps enhance efficiency in areas like
transportation and medication distribution. IPS can be utilised in an industrial
environment for inventory management, monitoring resource mobility, and
optimising manufacturing processes. It also contributes to reducing losses and
enhancing worker safety while facilitating the implementation of the e-kanban, a
digital version of the traditional kanban system used in lean manufacturing.
Wireless Bluetooth Low Energy (BLE) is an emerging IPS technology and the Ultra
Wideband (UWB) technology. The precision and reliability of UWB technology have
contributed to its increasing popularity, and it is expected to revolutionise indoor
object positioning and tracking, offering precise positioning accuracy within about ten
centimetres (Samama, 2019). That makes it ideal for indoor object-tracking
applications that require meter-level accuracy, such as asset tracking, inventory
management, and indoor navigation systems.
An essential factor to be considered pertains to the utilisation of data aggregators,
which allow for the merging and processing of outdoor and indoor object-tracking
data into a comprehensive and valuable source of information by incorporating
location data with supplementary particulars like object specifications, accountable
individuals, expiration dates, acceptable impact forces during handling, permissible
temperature ranges, and other related data, a remarkably versatile system can be
devised (Wang et al., 2022). This system not only tracks the locations of objects but
also facilitates the efficient management of business operations.
3.3. The Usage of IOT Data Aggregators
The task of collecting, organising, and analysing data received from various IoT
devices, sensors and actuators is managed by IoT data aggregators. These platforms
or systems play a central role in this process. The devices that fall within the realm of
IoT range from intelligent appliances and wearable devices to industrial machinery
and environmental sensors. The capacity of IoT data aggregators lies in their ability
to acquire data from diverse devices, regardless of their type or manufacturer, and
consolidate it into a centralised location. This consolidation simplifies the
management and analysis of the data. Aggregators can gather information from
multiple sources, integrate it, and provide a unified perspective of the gathered
intelligence.
Apart from consolidating data, these platforms often provide data cleansing,
normalisation, and enrichment tools similar to ETL (Extract, Transform and Load)
tools in Business Intelligence applications. These tools ensure the data's accuracy,
consistency, and usability for analysis purposes. Many applications can "clean" the
data and transform it into a format suitable for analysis (Lindel, 2020). Furthermore,
IoT data aggregators may also offer storage capabilities for organisations to
accumulate vast amounts of IoT data for future utilisation.
According to a study by Trappey et al. (2017), IoT data aggregators enable the
integration of diverse data from various sources, including sensors, actuators, and
smart devices. This integration occurs in a unified and standardised format,
simplifying extracting meaningful insights and deriving effective intelligence
(Trappey et al., 2017). By collecting and organising data from a variety of IoT devices,
aggregators enhance the visibility and accessibility of data, thereby enabling more
efficient data analysis. IoT data aggregators often incorporate advanced analytic
techniques like machine learning and artificial intelligence to identify patterns, trends,
and anomalies within the collected data. That enables real-time decision-making and
predictive analytics. This capability proves particularly valuable in domains that rely
heavily on timely and accurate data analysis. Therefore, IoT data aggregators serve as
critical components within the IoT ecosystem, facilitating the extraction of insights
from the massive volumes of data generated by interconnected devices.
It is worth noting that specific academic works even acknowledge that data
aggregators can exist in hardware units, such as routers, access points, gateway
devices, various stationary devices and even drones (Sharma et al., 2022). Notably,
drones possess the unique ability to surveil inaccessible or hazardous locales, enabling
them to gather information from sensors incapable of transmitting data across
substantial distances due to their inherent technological limitations (e.g., WiFi,
Bluetooth, RFID, and the like).
For an IoT network to be considered robust, it needs to possess the capability of
handling malfunctions without compromising its connectivity. Reliable networks are
necessary because they establish a dependable foundation for inter-device
communication. IoT systems' nodes, or vertices (Dagdeviren et al., 2022), are
typically interconnected through wireless channels and engage in message exchange.
Consequently, when relay nodes encounter failures, data transmission between nodes
can be disrupted, resulting in the inefficient utilisation of various resources. Thus, the
underlying communication infrastructure of a reliable IoT network should be
equipped with the ability to withstand failures and ensure the connectivity of active
nodes.
There is a multitude of challenges that must be taken into account when we are
speaking of network communication among IoT devices. One particular issue that
stands out prominently is the need for interoperability among diverse devices. This
limitation is often acknowledged as a crucial obstacle that needs to be addressed to
establish a network that is both seamless and free from vendor restrictions
(Rathanasalam et al., 2020), as well as a need for communication protocols in
connecting devices and applications. These protocols enable smooth data exchange,
establish device address schemes, and determine packet routing strategies (Mahbub,
2022). They also include functions like sequence control and flow control for optimal
communication. Within the IoT realm, unidirectional and bidirectional
communication among various devices must be logged in the database of a data
aggregator. That enables the consolidation of all the data in one central location,
thereby facilitating the preparation of reports using business intelligence analytical
tools.
Since the data arrives in real-time to the aggregator, tracking Key Performance
Indicators (KPIs) even daily is possible. KPIs can be identified and established at
different levels, including daily benchmarks, and are commonly utilised to monitor
and evaluate the accomplishments of organisational goals or projects. They can be
established long-term to assess overall performance and track daily or weekly
outcomes and progress at shorter-term levels. With enough computing power, stored
data, and a good selection of long-term, middle term and short-term KPIs, we expect
management to have a pretty good view of how business processes are conducted in
almost real-time.
3.4. Exploring the Potential of Data Aggregators in Outdoor Object Positioning
and Driving Quality Analysis
For this paper, we will analyse the data on outdoor object positioning. The situation
with indoor object positioning of objects is similar, with certain limitations. Indoor
tracking analysis, which relies on data from accelerometers (before mentioned IMUs),
can provide valuable information about how goods are manipulated inside the
warehouse. That data can be of high value if the business entity deals with sensitive
goods. Likewise, additional information (besides g-forces) is obtainable from other
types of sensors such as temperature and humidity sensors (if it is a heat and moisture-
sensitive goods such as food), magnetic and PIR sensors, identification beacon sensors
and the like.
For this paper, data from GNSS satellite tracking of eight vehicles equipped with
Teltonika 4G FMC230 GPS/Glonass/Galileo devices will be analysed for outdoor
object tracking. These devices are provided with additional 3-axis accelerometers that
transmit location and acceleration data in real-time. The accuracy of location is
achieved through simultaneous interpolation of 10-19 satellites. All the data is then
aggregated in the Fleet Management System (FMS) Wialon, developed by Gurtam.
Based on predefined rules, the system determines the driving quality for each vehicle
individually. The resulting report assigns a driving quality score on a scale of 1-10,
where a higher grade indicates driving that adheres more closely to safety regulations,
speed limits, and G-force related to acceleration, braking, and turning. Violations
related to speeding are penalised based on the specific road limits applicable to the
vehicle's route. For each car, expected g-force values are defined, and penalties for
exceeding the expected ranges are also established based on the vehicle type.
Acceleration calculations involve several data points, including:
• the unit's location,
• initial and final speed values,
• travel time between two points,
• the unit's movement direction, and
• particular parameters received from the device (G-forces).
Table 1. presents the chosen tangible values used to calculate and report the quality
of driver behaviour.
Table 1 Acceleration values for driving quality assessment
Name
Criterion
Min. value
Max. value
Penalty
Acceleration:
extreme
Acceleration
0.4g
2000
Acceleration:
medium
Acceleration
0.31g
0.4g
1000
Brake: extreme
Braking
0.35g
2000
Brake: medium
Braking
0.31g
0.35g
1000
Harsh driving
Reckless driving
0.3g
300
Speeding:
extreme
Speeding
41 km/h
5000
Speeding:
medium
Speeding
21 km/h
21 km/h
2000
Speeding: mild
Speeding
10 km/h
21 km/h
100
Turn: extreme
Turn
0.4g
1000
Turn: medium
Turn
0.31g
0.4g
500
Source: own research
This table shows the criteria and acceleration values used to assess driving quality.
The criteria include acceleration, braking, reckless driving, speeding, and turning.
Each measure has a range of values representing thresholds for specific driving
behaviours. Penalties are assigned based on behaviour severity, with higher penalties
for extreme behaviours. For example, extreme acceleration (>0.4g) and extreme
braking (>0.35g) receive a penalty of 2000. Medium acceleration and braking (0.31g-
0.4g for acceleration, 0.31g-0.35g for braking) receive a penalty of 1000.
Reckless driving (acceleration >0.3g) receives a penalty of 300. Speeding is
categorised as extreme, medium, and mild. Extreme speeding (>41 km/h) receives the
highest penalty of 5000, while medium speeding (21 km/h) and mild speeding (10
km/h-21 km/h) receive penalties of 2000 and 100, respectively. Extreme and medium
turning behaviours are also penalised. Extreme turning (acceleration >0.4g) receives
a penalty of 1000. Medium turning (0.31g-0.4g) gets a penalty of 500.
In summary, this table provides an overview of how driving behaviours are assessed
and penalised based on severity and impact on driving quality. The aim is to
discourage dangerous driving and promote safe and responsible behaviour.
Analysis was executed using data gathered from one week-long drive, thereby
revealing a comprehensive ranking of the driving quality of the eight vehicles
mentioned above. Through an examination of this data, valuable insights regarding
the driving quality of these individual vehicles are revealed, thereby facilitating well-
informed business decisions concerning vehicle servicing requirements, as well as the
potential for supplementary driver incentives for those who attain higher ranks or,
conversely, measures to driver penalty if it is necessary. By correlating this ranking
with the fuel consumption (assuming that all vehicles are exact type, model, and
engine power), one can present valuable information regarding the potential for
minimising overall business expenses for each vehicle. This report cannot be attained
solely through GNSS tracker data but can be critical to business owners.
The results of the analysis and the final report are presented in Figure 1.
Figure 1 Driving quality assessment report
Source: own research
The analysis findings concerning driver behaviour and driving quality are presented
clearly, utilising a table for easy comprehension. Each GNSS device and its
corresponding g-sensors transmitted an average of 7,000 to 9,000 telemetry messages
to the system. A total data accumulation of approximately 2 megabytes occurred, with
approximately 1.3 megabytes transmitted and 0.7 megabytes received. These values
indicate that nearly 35% of the data traffic is communication overhead, encompassing
TCP/IP handshaking and maintaining an open TCP/IP connection. Considering the
information above, it becomes evident that this reporting process has significant
challenges from the CPU processing power necessary to handle this volume of data
and the mentioned calculations.
4. THE IMPACT OF DATA ANALYSIS ON KEY PERFORMANCE
INDICATORS
In today's digital age, data has become the lifeblood of organisations, fuelling growth
and driving strategic decision-making. With the exponential growth of data, Big Data
has emerged, revolutionising how businesses operate, compete and measure their
KPIs. Integrating Big Data with Key Performance Indicators (KPIs) has opened up
new avenues for organisations to measure and enhance their success. KPIs are not a
new concept to companies (Parmenter, 20015), and they "represent a set of measures
focusing on those aspects of organisational performance that are the most critical for
the current and future success of the organisation". By examining the integration of
accounting and economic indicators and the analysis of IoT data as a foundation for
KPIs, leveraging Big Data can facilitate informed decision-making and drive key
performance measurements in various industries.
In 1992, Kaplan and Norton introduced the concept of balancing performance
indicators and developed the Balanced Scorecard methodology, which translates
business objectives into indicators across different dimensions. This has led to various
operational approaches (Franceschini et al., 2019):
• the Balanced Scorecard method;
• the Critical Few method;
• the Performance Dashboards, and
• the EFQM (European Foundation for Quality Management) model,
Within data analytics, those models can easily be presented with a graphic user
interface that shows the up-to-the-minute state of KPIs.
Big Data has emerged as a crucial tool for measuring and evaluating success in various
domains since it refers to the vast amount of information generated from multiple
sources. Such sources are web, emails and digital communications, social media,
sensory data, and transaction records, and they provide opportunities for organisations
to gain insights and make data-driven decisions. According to Wagner-Pacifici, Mohr,
and Breiger (2015), Big Data analytics enables the identification and analysis of KPIs
that can help assess an organisation's success. By harnessing large datasets,
organisations can identify patterns, trends, and correlations that were previously
unattainable with traditional data analysis methods. These KPIs can range from
customer satisfaction metrics to financial performance indicators, depending on the
organisation's specific objectives.
Furthermore, Big Data analytics can also facilitate predictive modelling and
forecasting, allowing organisations to anticipate future trends and make proactive
decisions. Associated KPIs have inevitably become indispensable tools for measuring
and evaluating success, providing organisations with valuable insights and
opportunities for improvement. (Wagner-Pacific et al. 2015)
As Mikalef, Krogstie, Pappas, and Pavlou (2020) also highlighted, Big Data analytics
enables organisations to identify key performance indicators (KPIs) crucial for
measuring their success. Organisations can identify patterns and correlations by
analysing large volumes of data to help them define meaningful KPIs. Furthermore,
Big Data analytics allows organisations to monitor and track these KPIs in real-time,
providing timely insights and the ability to make data-driven decisions. This ability to
leverage Big Data for KPI measurement, as stated by authors, has been shown to
impact organisational performance positively and can lead to increased
competitiveness and profitability.
According to a study by Gandomi and Haider (2015), Big Data analytics allows
companies to identify key performance indicators (KPIs) crucial in evaluating their
success. By "harnessing the power of big data", businesses can measure sales,
customer satisfaction, and operational efficiency metrics to assess their performance
and make data-driven decisions. The study emphasises that big data analytics provides
organisations with real-time and accurate information, enabling them to monitor their
KPIs effectively and make timely adjustments to their strategies. Therefore, big data
plays a pivotal role in helping businesses identify and evaluate key performance
indicators, leading to improved decision-making processes and overall success in
today's data-driven world.
As Wagner-Pacifici, Mohr, and Breiger (2015) also pointed out, big data provides
organisations with much information that can be used to gain insight into the
company's operations and to make the so-called informed decisions. With the
proliferation of digital technologies and the Internet, vast amounts of data are
generated and collected daily. As the researchers conclude (Lindell, 2015), with the
emergence of digital technologies and the Internet, large data sets have become vital
in measuring KPIs and enabling data-based decision-making and business
optimisation. The analysis of Big Data is significantly impacted by Neural Language
Processing (NLP), which can also be used in this scenario. There are several ways in
which NLP can enhance the analysis of extensive datasets (Sharma et al., 2022).
In the previous text, sources are presented that point to a long-known fact: for business
analysis, it is necessary to have good data sources and reliable metrics and
methodologies by which these data are analysed and presented. This paper presents
an example of data analysis within the logistic process of vehicle monitoring and the
combination of indoor and outdoor object positioning. The possibilities arising from
data aggregation (merging) led to introducing a concept known as Intelligent Logistics
System Based on IoT (Wang et al., 2022).
5. DISCUSSION AND FUTURE CONSIDERATIONS
This paper aims to help understand the vital role played by IoT data aggregators in
transforming business processes, particularly in sectors where an object positioning
system (indoor and outdoor) is a must, like logistics and manufacturing, retail and
even agriculture. The findings indicate that these data aggregators play a significant
role in enhancing the possibility of object tracking by collecting, processing, and
analysing data from IoT devices. That enables real-time tracking and more than just
analysis of past movements. Furthermore, it demonstrated that the contribution of data
aggregators extends to improving KPI monitoring by providing real-time performance
metrics. That facilitates proactive decision-making, operational efficiency and
predictive maintenance.
There are numerous possible implementations in practice. Some specifics would be
energy management (this can lead to significant cost savings and a reduced
environmental impact), Agriculture (increased crop yields and reduced costs),
Environmental Monitoring (improved environmental conservation efforts) and so on.
Despite the numerous benefits, it is crucial to address technical challenges associated
with data aggregation, such as interoperability and vendor-free technology.
Integrating Big Data with Key Performance Indicators (KPIs) significantly impacts
how organisations measure their success. The abundance of information from diverse
sources empowers organisations to gain valuable insights and make informed
decisions based on data. By leveraging Big Data analytics, organisations can uncover
previously undiscovered patterns, trends, and correlations, facilitating the
identification and analysis of KPIs. These indicators encompass a wide range of
metrics, covering aspects from customer satisfaction to financial performance,
providing organisations with a comprehensive understanding of their business.
Big Data analytics enables predictive modelling and forecasting. That analytics
empower organisations to anticipate future trends and make proactive decisions.
Despite the numerous benefits offered by IoT data aggregators, several technical
challenges need to be addressed, including data security and privacy, interoperability
and cost-effectiveness. Future research should focus on solutions that fully enable the
potential of IoT data aggregators as database storage for future analysis while
considering the associated challenges. That may be done with numerous systems like
Apache Pulsar, NATs, RabbitMQ, Apache Flink, Samza, Redis Streams, Apache
NiFi, Flume and, for example, RocketMQ. Many of these systems/platforms integrate
required data aggregation possibilities, but more research should focus on
standardising needed functions. One of the open questions is whether open-source
distributed data streaming platforms like Apache Kafka can provide all the required
functions. It is designed for high-throughput, fault-tolerant, and scalable data
streaming, allowing large volumes of data to be processed and aggregated in real-
time. Also, it can collect, store, and process data from various sources, making it
suitable for use as a data aggregator in big data and real-time analytics applications.
6. REFERENCES
Al-
Turjman, F., Yadav, S. P., Kumar, M., Yadav, V., & Stephan, T. (Eds.).
(2022). Transforming Management with AI, Big-Data, and IoT. Springer. p.17-
50.
Arora, V., Nawab, F., Agrawal, D., & Abbadi, A.E. (2017). Multi-
representation Based Data Processing Architecture for IoT Applications. 2017
IEEE 37th International Conference on Distributed Computing Systems
(ICDCS), 2234-2239.
Babu, S., Reddy, S.M., & Krishna, V.V. (2017). PERFORMANCE
ANALYSIS OF DATA PROTOCOLS OF INTERNET OF THINGS: A
QUALITATIVE REVIEW Anusha.M,.
Bakhru, K. (2005). A seamless tracking solution for indoor and outdoor
position location. 2005 IEEE 16th International Symposium on Personal,
Indoor and Mobile Radio Communications, 3, 2029-2033 Vol. 3.
Dagdeviren, Z. A., & Akram, V. (2022). Connectivity estimation approaches
for Internet of things-enabled wireless sensor networks. In Emerging Trends in
IoT and Integration with Data Science, Cloud Computing, and Big Data
Analytics (pp. 104-122). IGI Global.
Dobrovolskis, A., Janavičiūtė, A., Kazanavicius, E., & Liutkevičius, A. (2018).
METHOD FOR INDOOR HUMAN POSITION TRACKING USING
MULTIPLE DEPTH SENSORS. EPH - International Journal of Science And
Engineering.
Gerdisen, Y., Solid, V., Wahyudi, O., Hutabarat, D.P., & Hedwig, R. (2014).
Human Tracking in a Certain Closed Area based on Global Positioning System
(GPS).
Farrell, R., Yuan, X., & Roy, K. (2022). IoT to Structured Data (IoT2SD): A
Big Data Information Extraction Framework. 2022 1st International
Conference on AI in Cybersecurity (ICAIC), 1-6.
Nazemzadeh, P., Fontanelli, D., & Macii, D. (2013). An indoor position
tracking technique based on data fusion for ambient assisted living. 2013 IEEE
International Conference on Computational Intelligence and Virtual
Environments for Measurement Systems and Applications (CIVEMSA), 7-12.
Franceschini, F., Galetto, M., Maisano, D., & Neely, A. D. (2019). Designing
performance measurement systems. Management for professionals.
Gandomi, A., & Haider, M. (2015). Beyond the hype: Big data concepts,
methods, and analytics.
International journal of information
management, 35(2), 137-144.
Huang, L., He, B., & Zhang, T. (2010, March). An autonomous navigation
algorithm for underwater vehicles based on inertial measurement units and
sonar. In
2010 2nd International Asia Conference on Informatics in Control,
Automation and Robotics (CAR 2010) (Vol. 1, pp. 311-314). IEEE.
Hutabarat, D.P., Hendry, H., Pranoto, J.A., & Kurniawan, A. (2016). Human
tracking in certain indoor and outdoor area by combining the use of RFID and
GPS. 2016 IEEE Asia Pacific Conference on Wireless and Mobile
(APWiMob), 59-62.
Kumar, A., Kumar, S., Lal, P., Saikia, P., Srivastava, P. K., & Petropoulos, G.
P. (2021). Introduction to GPS/GNSS technology. In GPS and GNSS
Technology in Geosciences (pp. 3-20). Elsevier.
Lai-wu, Y., Deyun, C., & Hongwei, Z. (2011). Research on key technology of
Internet of Things based on FRID. 2011 International Conference on
Mechatronic Science, Electric Engineering and Computer (MEC), 355-358.
Lindell, J. (2020). Analytics and big data for accountants. John Wiley & Sons.
Mahbub, M. (2022). An Overview of IoT Infrastructure Architecture, Enabling
Technologies, Issues, Integration of Cloud, and Simulation Tools. Emerging
Trends in IoT and Integration with Data Science, Cloud Computing, and Big
Data Analytics, 20-38.
Malier, L., Cathelin, A., Cesana, G., & Pailleur, L.L. (2016). Low power
advanced digital technologies to enable Internet of Things. 2016 46th
European Solid-State Device Research Conference (ESSDERC), 15-16.
Mikalef, P., Krogstie, J., Pappas, I. O., & Pavlou, P. (2020). Exploring the
relationship between big data analytics capability and competitive
performance: The mediating roles of dynamic and operational
capabilities. Information & Management, 57(2), 103169.
Murakami, K., Matsuo, K., Hasegawa, T., Nohara, Y., Kurazume, R., & Ahn,
B. W. (2010, November). Position tracking system for commodities in an
indoor environment. In SENSORS, 2010 IEEE (pp. 1879-1882). IEEE.
Parmenter, D. (2015).
Key performance indicators: developing, implementing,
and using winning KPIs. John Wiley & Sons.
Perros, H. G. (2021). An Introduction to IoT Analytics. CRC Press.
Rathanasalam, R., & Kanagasabai, K. (2020). Data dissemination with
interoperability in IoT network. International Journal of Communication
Systems, 33(15), e4513.
Rizos, C. (2005, September). Trends in geopositioning for LBS, navigation
and mapping. In Proceedings of Int. Symp. & Exhibition on
Geoinformation (pp. 27-29).
Rodrigues, R. S., Pasin, M., & Machado, R. (2017, September). Indoor
position tracking: An application using the Arduino mobile platform. In 2017
10th IFIP Wireless and Mobile Networking Conference (WMNC) (pp. 1-8).
IEEE.
Saleem, T.J., Zahra, S.R., & Chishti, M.A. (2020). Data Analytical Framework
for Internet of Things. 2020 International Conference on Decision Aid
Sciences and Application (DASA), 109-115.
Samama, N. (2019). Indoor positioning: technologies and performance. John
Wiley & Sons.
Sharma, A., Singh, P. K., Nikashina, P., Gavrilenko, V., Tselykh, A., &
Bozhenyuk, A. (2023). Role of UAVs in Delivering the Patient Samples from
One Hospital to Different Hospital. In IoT, Big Data and AI for Improving
Quality of Everyday Life: Present and Future Challenges: IOT, Data Science
and Artificial Intelligence Technologies (pp. 139-152). Cham: Springer
International Publishing.
Sharma, R., Agarwal, P., & Arya, A. (2022). Natural language processing and
big data: a strapping combination. In New Trends and Applications in Internet
of Things (IoT) and Big Data Analytics (pp. 255-271).
Cham: Springer
International Publishing.
Sokolova, N., Borio, D., Forssell, B., & Lachapelle, G. (2010). Doppler
rate measurements in standard and high sensitivity GPS receivers:
Theoretical analysis and comparison. In 2010 International Conference
on Indoor Positioning and Indoor Navigation (pp. 1-9). IEEE.
Trappey, A. J., Trappey, C. V., Govindarajan, U. H., Chuang, A. C., & Sun, J.
J. (2017). A review of essential standards and patent landscapes for the Internet
of Things: A key enabler for Industry 4.0. Advanced Engineering
Informatics, 33, 208-229.
Uuddin, M.A., Mansour, A., Jeune, D.L., & Aggoune, H.M. (2017).
Agriculture internet of things: AG-IoT. 2017 27th International
Telecommunication Networks and Applications Conference (ITNAC), 1-6.
Wagner-Pacifici, R., Mohr, J. W., & Breiger, R. L. (2015). Ontologies,
methodologies, and new uses of Big Data in the social and cultural
sciences. Big data & society, 2(2), 2053951715613810.
Wang, T., Chen, H., Dai, R., & Zhu, D. (2022). Intelligent Logistics System
Design and Supply Chain Management under Edge Computing and Internet of
Things. Computational Intelligence and Neuroscience, 2022.
