=Paper=
{{Paper
|id=Vol-3058/paper46
|storemode=property
|title=A Review of IoT Models
|pdfUrl=https://ceur-ws.org/Vol-3058/Paper-073.pdf
|volume=Vol-3058
|authors=Manjeet Singh Pangtey,Dr. Vishal Gupta
}}
==A Review of IoT Models==
https://ceur-ws.org/Vol-3058/Paper-073.pdf
A Review of IoT Models
Manjeet S.Pangtey1 and Vishal Gupta2
1
G B Pant Government Engineering College, Computer Science and Engineering Department, Okhla Phase 3,
New Delhi, India
2
NSUT-East Campus (Formerly AIACT&R), Computer Science and Engineering Department, Geeta Colony,
New Delhi, India
Abstract
IoT is seen almost everywhere and the choices are numerous. At the same time if we want to
think of an ecosystem the goal should be such that independent IoT models can be taken
under one umbrella. Various models can communicate with each other, that should be the
goal of an IoT model. Any new IoT model should be able to attach itself with an existing
model so that the ultimate purpose of advancement of IoT, which is to connect everything,
can be made possible. Communication protocols play an important role in the area of IoT.
These communication protocols may vary with respect to the application area and domain.
This survey discusses a variety of IoT Models and their approach for the implementation.
The paper discusses the differences between the models and proposes the scope of scalability
and interoperability in the various Iot models. Analysis of those models with the target of
finding the factors stopping IoT models to communicate with each other is presented then
there is a discussion presented for scope of scalability and interoperability in the existing
models.
Keywords 1
Internet of Things (IoT), Architecture, Scalability, Interoperability.
1. Introduction
IoT is getting more limelight because the main goal of using IoT is to resolve the existing issues of
connectivity, minimize the operating cost and sometimes fast action taken on the particular situation
such as medical, farming, transportation, industry, fitness, home automation, smart city etc. In doing
so, various implementations of IoT are laid out. Some are implemented and many are proposed. For
example nowadays the concept of smart city is acknowledged highly and IoT has a key role in
fulfilling the concept of smart city. There are various models of IoT that are acting at multiple
applications such as transport management, security and surveillance management, day-to-day facility
management, medical requirement management and so on. If these models are not able to
communicate with each other then there will be a huge dependency on manual intervention for taking
day-to-day decisions as well as critical decisions. To deal with such requirements, a model of
technologies termed as ecosystem is getting the attraction. An ecosystem is like an umbrella which
consists of various technical pillars provided to support the various technical requirements and since
all the pillars are under one umbrella they can communicate with each other. Communication between
these various models is smooth and requires very less manual intervention. It is evident that all the
models in an ecosystem must have a standardization to make the meaning of ecosystem successful.
This standardization enhances the interoperability between different models. This is one of the
examples where we can see the importance of intra-model communication is highly important,
similarly there can be a number of scenarios where we need this kind of support.
International Conference on Emerging Technologies: AI, IoT, and CPS for Science & Technology Applications, September 06–07, 2021,
NITTTR Chandigarh, India
EMAIL: man74pangtey@gmail.com(A. 1); vishalgupta@aiactr.ac.in(A. 2)
ORCID: https://orcid.org/0000-0001-6948-7425 (A. 1)
©2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
To establish communication between two or more than two devices, there should be a
compatibility of technologies between them. In the current scenario when the technology is changing
dynamically due to multiple players in the market, interoperability becomes a significant challenge for
all. In practice where a variety of platforms, networks, programming languages, syntactic and
semantic varieties are present, it becomes challenging to come to one common standard. In this paper
we have analyzed various approaches to handle these challenges based on various criteria. Issues
related to interoperability are addressed by many researchers. This paper is an attempt to analyse the
available methodologies and techniques used for handling interoperability and identifying major
issues in having a generic model of IoT to resolve interoperability issues.
2. Related Work
Various studies and research work related to interoperability and IoT models and patterns are
evaluated under this study and their highlights are presented in this paper. With the increasing role of
IoT, huge attempts have been made and various models are presented to tackle interoperability issues.
In one of the major studies done by Jonggwan et al [1], two famous platforms- oneM2M and
FIWARE were introduced. These two platforms are popular and widely used in smart city projects.
The oneM2M and FIWARE provides functionalities and a platform for IoT devices and is an
ecosystem in themselves. There is no mechanism available to establish communication between them
and a separate mechanism is required to make the communication possible. So a well designed
architecture is developed which translates the conversation between these platforms in their respective
terminologies.
A different architecture based on BlockChain is proposed by Hong-Ning Dai et al [2] also
termed as BCoT. It is suggested that using BlockChain technology, interoperability can be enhanced.
BlockChain data is distributed among IoT devices, cloud servers and edge servers [3,4,5]. The author
[6] has pointed out that the diversity among the technologies may be because of their nature of
handling MAC and Physical layers, variety of bands at which they work, the size of data supported
for transmission, various communication protocols at application layer, variety of error correction
mechanisms and variation in security handling.Some other methods of handling security is presented
in [33, 34] using semantics at sensor level and time-stamping for authentication. One of the recent
studies [7] points out that IoT models also require regular rigorous testing for continuous
improvement so automated testing tools are required. Basically using these testing tools, these models
can be tested against the interoperability level by validating the standards they follow. The paper
suggests conformance testing to find out the level of standardization followed in an application.
In this study, few IoT models were analyzed to understand the generic pattern of IoT models. One
such model is given in [8] in which the author has discussed a smart railway management system.
Manual management is definitely time consuming, resource consuming and costly. To solve these
complexities, an IoT model is proposed in which the railway structure is categorized into 3 parts (1.
Buildings, 2. Railway tracks and facility 3. Signaling and Communication technologies). A
conditional based maintenance system, also known as CBM [26, 27] is proposed which basically
activates and takes decisions on the basis of an event. Some other studies related to smart railways are
givenin [12,13].In [6], an IoT model to study air quality in the Indo gangetic plains due to crop
burning and other factors such as Diwali, etc is presented. Related IoT models with respect to air
quality are presented in [14, 15, 16, 17, 18]. In the field of electrical grid, IoT can play a crucial role
and this application is called smart grid [10]. Some other IoT models in the area of smart grid are
presented in [19, 20, 21, 22]. Iot models are also available in the field of tracking such as parking
status tracking or tracking of any movable object or person. One such example is presented in [11].
Some other tracking models are presented in [23,24,25]. In the next section detailed discussion and
analysis is presented of the reviewed IoT models classified on various parameters laid out in this
study. These set of parameters are:
1. Variety of IoT Models: Four generic categories are selected- transportation, air quality,
electrical grid, tracking.
2. Approach to Implement IoT Models: The approach for implementing an IoT model for
various models are extracted and analyzed.
3. Factors stopping IoT models to communicate with each other: This parameter highlights
factors which are hindrance for interoperability with respect to individual models.
4. Scope of Scalability and Interoperability: Individual models for the scope of scalability and
interoperability are reviewed.
3. Analysis of existing IoT models
In this section detailed discussion of IoT models based on the criteria as figured out in section 2 is
presented, i.e. below mentioned criteria is presented. These criteria’s are (a) Variety of IoT models,
(b) Approach to implement IoT models, (c) Factors stopping IoT models to communicate with each
other and (d). Scope of scalability and interoperability in the existing models
3.1. Variety of IoT Models
For our study, four categories of IoT application areas are selected such as transportation, air
quality, electrical grid, and tracking. Though in this work various authors are available in the selected
domains, the few latest and the selected ones are presented in Table 1.
Table 1
Area specific IoT models
Area Sub Area Author
Transportation Smart Railway Ohyun Jo et al. [8]
Railway monitoring Pengyu Li et al. [12]
Railway track defect detection NooraAlNaimi et al. [13]
Air Quality Air quality sensing and reporting Rohan Kumar Jha [14]
Air quality monitor Liaoyuan Zeng et al. [15]
Air quality monitoring system Ajitesh Kumar et al. [16]
ML based air pollution control Sharafat Ali et al. [17]
Air pollution monitoring system Swati Dhingra et al. [18]
Electric Grid Smart Grid Markel Iglesias et al. [10]
Power grid condition monitoring Tianxin Zhuang et al. [19]
Smart grid Yuke Li et al. [20]
Real time demand response in smart grid Ashish Kumar Sultania et al. [21]
Smart grid decision support tool Md. Rabiul Islam et al. [22]
Tracking Parking status and dog tracking Yi-Bing Lin et al. [11]
Real time object tracking AnisKoubaa et al. [23]
Real time laptop tracking NiritDatta et al. [24]
Container tracking near coastline SrikanthKavuri et al. [25]
3.2.Approach to Implement IoT Models
In this section, IoT models are observed for their architecture and design. Most of the traditional
methods of maintenance in rail transportations are routine maintenance which involves huge cost and
resources and does not provide any great value in case of any issues in between the routines. So
Condition-based maintenance also known as CBM based techniques are getting popular [26, 27].
General CBM based techniques again goes through a procedure such as Inspection, analysis,
prioritization, budgeting and execution. In this process most of the resources are exhausted up to
inspection and less remains for repair which seems wrong in some sense so some enhancement is
proposed in [8]. Enhancement towards more on repair work and reducing the manual intervention is
proposed in [8, 9, 10]. IoT devices are installed and data is captured in various forms and the decision
is taken after processing the data. As we can see in Table 2, models vary from each other mostly in
terms of technology such as LTE, LoRa, GSM and some are generic as they focus on data analytics
[13]. In Table 2, basic architecture preferred in the models and key technology used is listed. Basic
architecture is similar but the choice of sensors varies depending on the application.
Similarly, there are a number of IoT models available in the area of air quality [9] – [18]. Some of
the models are listed in Table 3 with the observation of their basic architecture and preferred
technology. Again basic architecture is followed and the variations among the models are at the
sensor level, network technology and database.
Due to licensed bands and licensed free bands, the technologies vary as per the application, as
smart grids are one of the mission critical applications [20] and to incorporate QOS, licensed bands
are preferred. Aim of a smart grid is to distribute power equally. Basic architecture and key
technology used for smart grids are listed in Table 4. Differences are at the communication level
between sensors and gateway and between gateway and server and cloud application and database.
Table 5, lists the Tacking based IoT models. Differences among models are application areas like
for drones we use ROS which is a Robot Operating System while non drone tracking models don’t
use ROS. Also variation can be found at the deployment of models in real time because depending on
the requirement it varies. Like in the case of real time object monitoring using drones, we need to set
up the drones manually. Cloud servers have multiple roles, like storage, interaction with the devices,
analytics of data etc.
Cost efficient IoT Models are in trend and that's why too high variation in technology is seen.
Table 2
Railway maintenance IoT models
Author Proposal Architecture Technology
Ohyun Jo et al. [8] Enhanced CBM Device Platform (Sensors) -> LTE, LoRa,
Gateway ->IoT Network -> NB-IoT
Platform Server
Pengyu Li et al. [12] Maintenance Free Sensors -> Readers -> GSM GSM
CBM Communicat-ion system ->
control center (Data hub)
NooraAlNaimi et al. [13] Railway track fault Camera -> Local Image ML
detection processor -> Cloud Server
Table 3
Air Quality based IoT Models
Author Proposal Architecture Technology
Rohan Kumar Air Quality Sensors -> Controller -> Cloud Server WiFi, Arduino
Jha [14] Reporting Uno
Liaoyuan Zeng Air Quality Monitor Sensors -> Controller -> Cloud Server Pycom,
et al. [15] LoRaWAN,TTN
Cloud server
Ajitesh Kumar Air Quality Sensors -> Controller -> Storage Thing Speak
et al. [16] Monitoring System Cloud storage.
Sharafat Ali et Low Cost Air Sensors -> Controller -> Cloud Storage LoRaWAN,
al. [17] Quality Monitor WiFi,
with ML based
calibration
Swati Dhingra et Air Pollution Sensor -> Processor -> Cloud Storage Ubidots as
al. [18] Monitoring Cloud Service
Table 4
Smart Grid based IoT Models
Author Proposal Architecture Technology
Tianxin Insulation Condition Sensors -> Controller -> Server NB-IoT, LoRa
Zhuang et al. Monitoring
[19]
Yuke Li et al. Grid monitoring Not provided NB-IoT
[20]
Ashish Kumar Real Time Response Grid Smart plug/ Demand Response NB-IoT, Z-
Sultania et al. Controller -> NB-IoT Network -> Wave,
[21] Cloud Server Raspberry Pi
Md. Rabiul Decision Support Tool for Sensor/Meter -> ZigBee, MQTT
Islam et al. Distributed Energy Sources Gateway/ZigBee -> Central
[22] and Electric Vehicles Controller/Server
Variation in cloud storage selection is observed among models. Use of Machine Learning for
variation of results is in study. Different scenarios such as mobile, remote, vast busy, wave, air, static,
mobility conditions are observed for variation in technology. Also due to power management, the
architecture may vary. Sensors also vary depending on their power consumption and other factors.
Further study can be also organized in terms of life of the sensors and scalability of architecture.
Controllers are also replaced with term gateway in terms of their functionality and location of uses.
Most of the models have the same objective and strive for more precision, better sensitivity etc. It is
observed that all the IoT models follow the layered IOT architecture, which is sensor layer,
networking layer and application layer.
Table 5
Tracking based IoT Models
Author Proposal Architecture Technology
Yi-Bing Lin et NB-IoT Talk Device Tracker Sensor -> Gateway - MQTT, NB-IoT
al. [11] >DataBase
AnisKoubaa et Real Time Object Tracking Dones -> Gateway -> ROS, MAV Link
al. [23] using Drones Cloud Services Communication protocol,
NiritDatta et Real time Motion sensor Sensor -> Gateway - STM32 Microcontroller, GSM
al. [24] and Alert System > Cloud Server
SrikanthKavuri Container Tracking Near Sensors -> Base NB-IoT
et al. [25] the Shore Vessel Station -> Server
3.3. Factors stopping IoT models to communicate with each other
In this study, in the area of railway maintenance, it is found that the architecture design has some
variations at the middle layer. Some models have data processing near to the sensor level and some
perform these activities at the cloud level. Overall, the architecture follows the standard IoT
architecture which is represented in figure 1. Depending on the model it is observed that choice of
sensor varies depending upon the model. In conditional based monitoring systems for trains various
types of sensor such as wireless sensors, constant powered sensors are basic differences. Due to these
basic differences it is obvious that there will be some changes in the architecture at the intermediate
levels. In the case of Air Quality based IoT models it is observed that most of the models are using
LoRaWAN which is understandable due to the properties of LoRaWAN. At the same time the choice
of cloud services also differs. Due to these choices the communication level and behavior between
sensors and gateway and between gateway and server varies. Another difference is the cloud
application.
Major difference observed while observing smart grid based models is the licensed and free
band as a result technological preference changes. At the same time NB-IoT is found as the preferred
choice for smart grid applications. Other basic differences are choice of cloud services, protocols for
data delivery. Similarly for tracking based IoT Models, NB-IoT is observed as the preferred choice
and at the same time choice of operating system, communication protocols and sensors is largely
varied.
3.4. Scope of scalability and interoperability in the existing models
The basic architecture of any IoT model remains unchanged which is Sensor - Gateway -
Database. Choice of sensor varies largely as per the requirement such as simple sensors, smart sensors
[28, 32]. The variations in communication model between Sensor - Gateway and Gateway - Cloud are
huge. Among the observed models NB-IoT and LoRaWAN are found preferred choices. Similarly for
communication protocols, MQTT and CoAP [29] are found popular choices rather than HTTP- Rest,
SOAP Web Services [30]. This behavior leads to standardization of IoT models and can be tested for
other scenarios so that these models can be scaled and become interoperable.
Figure 1: Standard IoT Architecture
4. Conclusion and future work
Various IoT Models under four categories are observed in this study and their analysis on specific
criteria is presented. All the IoT models are studied from the point of view of their architectural model
and technological preferences. During this study the approach to develop these models is observed
and possible scope of scalability and interoperability is presented for the considered models. The
observations of different models are carried out among four categories of IoT application as transport,
air quality, electric grid and tracking. To analyze the models under these categories, two specific
criteria were focused which is the basic architecture of the model and technology used.
Majority of IoT models follow basic IoT architecture which is Sensor - Gateway - Database.
It is observed that having common architecture does not provide scalability of the models and
interoperability among them. Scalability issue is mainly because of the application area since the IoT
application area is very huge and keeps changing. Even in the area of transportation, it is very difficult
to scale the existing application from railway to any other transportation field. In terms of
Interoperability observed devices can't be interoperable due to reasons such as different protocols
used for communication, variety of cloud storage and services available and due to security issues. It
leads us to standardization of IoT protocolsat various level of IoT model.
In this study we have some preferred technologies which can make the standardization of IoT
models somehow possible. These technologies are LoRaWAN, NB-IoT [31], CoAP [29], MQTT. In
future we will try to build up a standardize IoT model that uses the preferred technologies as pointed
out in this paper.
5. References
[1] J. An et al., “Toward Global IoT-Enabled Smart Cities Interworking Using Adaptive Semantic
Adapter,” in IEEE Internet of Things Journal, vol. 6, no. 3, pp. 5753-5765, June 2019.
[2] H. -N. Dai, Z. Zheng and Y. Zhang, “Blockchain for Internet of Things: A Survey,” in IEEE
Internet of Things Journal, vol. 6, no. 5, pp. 8076-8094, Oct. 2019.
[3] O. Novo, “Blockchain Meets IoT: An Architecture for Scalable Access Management in IoT,” in
IEEE Internet of Things Journal, vol. 5, no. 2, pp. 1184-1195, April 2018.
[4] M. A. Ferrag, M. Derdour, M. Mukherjee, A. Derhab, L. Maglaras and H. Janicke, “Blockchain
Technologies for the Internet of Things: Research Issues and Challenges,” in IEEE Internet of
Things Journal, vol. 6, no. 2, pp. 2188-2204, April 2019.
[5] D. Miller, "Blockchain and the Internet of Things in the Industrial Sector," in IT Professional,
vol. 20, no. 3, pp. 15-18, May./Jun. 2018.
[6] Q. M. Qadir, T. A. Rashid, N. K. Al-Salihi, B. Ismael, A. A. Kist and Z. Zhang, “Low Power
Wide Area Networks: A Survey of Enabling Technologies, Applications and Interoperability
Needs,” in IEEE Access, vol. 6, pp. 77454-77473, 2018.
[7] J. Hwang, A. Aziz, N. Sung, A. Ahmad, F. Le Gall and J. Song, “AUTOCON-IoT: Automated
and Scalable Online Conformance Testing for IoT Applications,” in IEEE Access, vol. 8, pp.
43111-43121, 2020.
[8] O. Jo, Y. Kim and J. Kim, “Internet of Things for Smart Railway: Feasibility and Applications,”
in IEEE Internet of Things Journal, vol. 5, no. 2, pp. 482-490, April 2018.
[9] A. Chauhan and R. P. Singh, “Poor air quality and dense haze/smog during 2016 in the indo-
gangetic plains associated with the crop residue burning and diwali festival,” in IEEE
International Geoscience and Remote Sensing Symposium (IGARSS), pp. 6048-6051, 2017.
[10] Iglesias Urkia, Markel &Urbieta, Aitor& Parra, Jorge &CasadoMansilla, Diego, “IEC 61850
meets CoAP: towards the integration of smart grids and IoT standards,” in ACM: Proceedings of
the Seventh International Conference on the Internet of Things, Article 3, pp. 1-9, 2017.
[11] Y. Lin, H. Tseng, Y. Lin and L. Chen, “NB-IoTtalk: A Service Platform for Fast Development of
NB-IoT Applications,” in IEEE Internet of Things Journal, vol. 6, no. 1, pp. 928-939, Feb. 2019.
[12] P. Li, Z. Long and Z. Yang, “RF Energy Harvesting for Batteryless and Maintenance-Free
Condition Monitoring of Railway Tracks,” in IEEE Internet of Things Journal, vol. 8, no. 5, pp.
3512-3523, March 2021.
[13] N. AlNaimi and U. Qidwai, “IoT Based on-the-fly Visual Defect Detection in Railway Tracks,”
inIEEE International Conference on Informatics, IoT, and Enabling Technologies (ICIoT), pp.
627-631, 2020.
[14] R. K. Jha, “Air Quality Sensing and Reporting System Using IoT,” inSecond International
Conference on Inventive Research in Computing Applications (ICIRCA), pp. 790-793, 2020.
[15] S. McGrath, C. Flanagan, L. Zeng and C. O'Leary, “IoT Personal Air Quality Monitor,” in 31st
Irish Signals and Systems Conference (ISSC), pp. 1-4, 2020.
[16] A. Kumar, M. Kumari and H. Gupta, “Design and Analysis of IoT based Air Quality Monitoring
System,” in International Conference on Power Electronics &IoT Applications in Renewable
Energy and its Control (PARC), pp. 242-245, 2020.
[17] S. Ali, T. Glass, B. Parr, J. Potgieter and F. Alam, “Low Cost Sensor With IoTLoRaWAN
Connectivity and Machine Learning-Based Calibration for Air Pollution Monitoring,” in IEEE
Transactions on Instrumentation and Measurement, vol. 70, pp. 1-11, Art no. 5500511, 2021.
[18] S. Dhingra, R. B. Madda, A. H. Gandomi, R. Patan and M. Daneshmand, “Internet of Things
Mobile–Air Pollution Monitoring System (IoT-Mobair),” in IEEE Internet of Things Journal,
vol. 6, no. 3, pp. 5577-5584, June 2019.
[19] T. Zhuang, M. Ren, X. Gao, M. Dong, W. Huang and C. Zhang, “Insulation Condition
Monitoring in Distribution Power Grid via IoT-Based Sensing Network,” in IEEE Transactions
on Power Delivery, vol. 34, no. 4, pp. 1706-1714, Aug. 2019.
[20] Y. Li, X. Cheng, Y. Cao, D. Wang and L. Yang, “Smart Choice for the Smart Grid: Narrowband
Internet of Things (NB-IoT),” in IEEE Internet of Things Journal, vol. 5, no. 3, pp. 1505-1515,
June 2018.
[21] A. K. Sultania, F. Mahfoudhi and J. Famaey, “Real-Time Demand Response Using NB-IoT,” in
IEEE Internet of Things Journal, vol. 7, no. 12, pp. 11863-11872, Dec. 2020.
[22] M. R. Islam, H. Lu, M. R. Islam, M. J. Hossain and L. Li, “An IoT- Based Decision Support
Tool for Improving the Performance of Smart Grids Connected With Distributed Energy Sources
and Electric Vehicles,” in IEEE Transactions on Industry Applications, vol. 56, no. 4, pp. 4552-
4562, July-Aug. 2020.
[23] A. Koubaa and B. Qureshi, “DroneTrack: Cloud-Based Real-Time Object Tracking Using
Unmanned Aerial Vehicles Over the Internet,” in IEEE Access, vol. 6, pp. 13810-13824, 2018.
[24] N. Datta, A. Malik, M. Agarwal and A. Jhunjhunwala, “Real Time Tracking and Alert System
for Laptop through Implementation of GPS, GSM, Motion Sensor and Cloud Services for
Antitheft Purposes,” in 4th International Conference on Internet of Things: Smart Innovation and
Usages (IoT-SIU), 2019, pp. 1-6, 2019.
[25] S. Kavuri, D. Moltchanov, A. Ometov, S. Andreev and Y. Koucheryavy, “Performance Analysis
of Onshore NB-IoT for Container Tracking During Near-the-Shore Vessel Navigation,” in IEEE
Internet of Things Journal, vol. 7, no. 4, pp. 2928-2943, April 2020.
[26] LIU X, LI J R, AL-KHALIFA K N, et al. “Condition-based maintenance for continuously
monitored degrading systems with multiple failure modes,” in IIE Transactions, vol. 45, no. 4,
pp. 422 – 435, 2013.
[27] Chen Chuang, LU Ningyun, et al. “Condition-based maintenance optimization for continuously
monitored degrading systems under imperfect maintenance actions,” in Journal of Systems
Engineering and Electronics, vol. 31, no. 4, pp. 841 - 851, Aug. 2020.
[28] Eugene Y. Song, Gerald J. FitzPatrick, et al., “Smart Sensors and Standard-based Interoperability
in Smart Grids,” in IEEE Sensors Journal, vol. 17, no. 23, pp. 7723 - 7730 July 2017.
[29] Z. Shelby, K. Hartke, and C. Bormann, “The constrained application protocol (CoAP),” in
Internet Eng. Task Force, Fremont, CA, USA, RFC 7252, Jun. 2014.
[30] C. Bormann and P. E. Hoffman, “Concise binary object representation (CBOR),” in Internet Eng.
Task Force, Fremont, CA, USA, RFC 7049, Oct. 2013.
[31] RashmiSharan Sinha, Yiqiao Wei, Seung-Hoon Hwang, “A survey on LPWA technology: LoRa
and NB-IoT”, in ICT Express, Volume 3, no. 1, pp. 14-21, 2017.
[32] Sejdiu, Besmir,et al. "Integration of Semantics Into Sensor Data for the IoT: A Systematic
Literature Review." IJSWIS vol.16, no.4 2020: pp.1-25.
http://doi.org/10.4018/IJSWIS.2020100101
[33] Tewari, Aakanksha and Brij B. Gupta. "Secure Timestamp-Based Mutual Authentication
Protocol for IoT Devices Using RFID Tags." IJSWIS vol.16, no.3 2020: pp.20-34.
http://doi.org/10.4018/IJSWIS.2020070102
[34] Salhi, DhaiEddine,et al. "Using Clustering for Forensics Analysis on Internet of
Things." IJSSCI vol.13, no.1 2021: pp.56-71. http://doi.org/10.4018/IJSSCI.2021010104