COVID-19 and Beyond: IoT Security and Privacy in Industrial Organizations
Mohd Khairul Nizam1*, S. B. Goyal1*
1 City University, Petaling Jaya, Malaysia
*Corresponding Author: [email protected], [email protected] Accepted: 15 April 2022 | Published: 1 May 2022
DOI:https://doi.org/10.55057/ijarti.2022.4.1.14
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Abstract: The world now has been covered by an invisible network of devices, known as The Internet of Things (IoT). The IoT has played a huge role since it was introduced. This trend is seen to proliferate when it is introduced in all fields and massively shift when the Covid-19 pandemic starts.
While the development saw increased productivity in some areas of the industry, some failed to strive due to sudden changes in social norms. Given the current situation, device developers have seized the chance and taken the initiative to develop IoT devices further, recognizing their enormous potential.
Nevertheless, as technology advances, security and privacy loopholes will be created, which can be taken advantage of to reap profits. Past and current research has found proper solutions to several issues plaguing end users. Still, researchers are developing a new security and protocols update due to progressive technology development over recent years. Industries are one of the areas that will be severely affected if this issue is ignored; not only can it affect individuals, but it can even affect the country, especially in the post-COVID-19 era. This paper will look at the industry angle of security and privacy used in daily activities. With all the research done to date, some information can be gathered and analyzed to measure somehow the security level of IoT security and privacy. Hopefully, these findings will give some insight into industries on IoT security and privacy level, allowing the industries to achieve a more secure footing as they recover from the shortcoming of the Covid-19 pandemic.
Keywords: IoT, IoT security, IoT privacy, Covid-19, IoT guidelines
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1. Introduction
The Internet of Things (IoT) is a concept that allows things to perceive, communicate, analyze, and act or react independently to people and other machines. While sensor technology has been around for decades, today's sensors are more competent, cheaper, and connected than ever before, allowing for new ways to collect real-time data generated by machines, humans, or any connected device. IoT applications face several obstacles: seamless data aggregation, mobility, etc., and, most importantly, security and privacy.
IoT Device Overview
The Internet of Things (IoT) (Rajawat et al 2022) combines instruments, applications, and other technologies that enable them to communicate to and share information between devices or systems over the Internet. Turning standard gadgets into IoT smart device require two things which are:
• Has internet connection ability
• Technology integrated, such as functional applications, actuators, sensors, etc.
IoT protocols and standards
Since there is no international compatibility standard for Internet of Things devices, developers invented devices according to their usage and expertise. However, some based on traditional protocols and frameworks are being used on their devices.
Fig. 1.1: New IoT Standards
Fig. 1.2: Standard IoT framework
The protocols can also be classified as follows based on their architecture layers:
Table 1.2 Protocol classification by architecture layer
Layer Protocols / Communication Standards
Infrastructure IPv4/IPv6, 6LowPAN, UDP, ROLL/RPL, QUIC, Aeron, uIP, DTLS, NanoIP, CCN, TSMP
Identification uCode, EPC, URIs, IPv6 Communications /
Transport
Cellular/2G/3G/4G/5G, Bluetooth/BLE, LoRA, SigFox, ZigBee, Wi-Fi, Z-Wave, 802.154, WirelessHart, ISA100.11a, DigiMesh, NFC, LoRaWAN,
Discovery mDNS, Physical Web, HyperCat, UPnP, DNS-SD Data Protocols
MQTT, MQTT-SN, CoAP, SCMP, AMQP, STOMP,
Websocket, XMPP, Node, Mihini/M3DA, DDS, JMS, LLAP, LWM2M, SSI, ONS 2.0, REST, HTTP/2, JavaScript
Device Management OMA-DM, TR-069
Semantic W3C, Wolfram Language, JSON-LD, SENML, LsDL, SensorML, IOTDB
Multi-layer Frameworks
IEEE P2413, IoTivity, Alljoyn, IPSO Application Framework, Weave, Homekit
Covid-19 Overview
In recent times, the IoT has established itself as a compelling study issue across a broad range of educational and industrial fields, most notably in medicine. In the case of COVID-19, IoT- enabled/connected devices/applications are used to reduce the risk of COVID-19 spreading to others by performing timely detection, providing care, and adhering to prescribed practices following clinical outcomes. As a result, it has made significant strides in combating the illness (Nasajpour et al., 2020).
IPv6 over over Low- Power Wireless
Personal Area Networks (6LoWPAN)
LiteOS ZigBee OneM2M
Data Distribution Service (DDS)
Advanced Message Queuing Protocol
(AMQP)
Constrained Application Protocol
(CoAP)
Long Range Wide Area Network
(LoRaWAN)
Amazon Web Services (AWS) IoT
Arm Mbed IoT
Microsoft's Azure IoT
Suite
Google's
Brillo/Weave Calvin
2. Literature review Architecture
Since there is no consensus on the correct IoT architecture, several types of architectures have been proposed by researchers around the world (Abeer & Haya, 2018; Sethi & Sarangi, 2017; Weyrich &
Ebert, 2016; Goyal et al 2022).
Table 2.1: A definition of IoT architecture.
Architecture Layer Function
5-layer architecture
Perception layer
purely physical, sensors in devices to perceive and collect data within preset parameters
Network layer network to connect smart devices and servers, to transfer and process the sheer amount of information received.
Application layer
Submitting services tailored to specific applications to the end-user defines what application is being deployed, e.g., smart cities and smart homes.
transport layer the route that transmits data collected from the perception layer to the processing layer
Processing layer
layer that collects, analyzes, and processes the big chunk of information received from the transport layer. A variety of technologies is deployed to process pieces of information in this layer.
IoT security and privacy
IoT devices generally lack security capabilities due to the fact that the components that makeup IoT devices are typically low-cost and mass-produced. Therefore, the security and integrity of the majority of IoT devices are in doubt (Abdul-Ghani & Konstantas, 2019). Frustaci et al. (2018) discussed the common risks that can bring possible security issues, described in Table 2.2 (Frustaci et al., 2018).
Table 2.2: The common threat in IoT architecture.
Architecture Layer Threats
Perception
Data transit Attack Routing attacks Dos attacks Impersonation Physical attacks Transportation
Data transit attacks DoS attacks Routing attacks Application
Malicious code injection Data leakage
Dos attacks
Limited device resources, a complicated ecosystem, privacy's contextuality, and a never-ending life cycle process were cited as barriers to defining, implementing, and analyzing IoT security and privacy principles. According to W. Zhou et al. (2019), eight IoT features impact security and privacy issues as depicted in Table 2.3
Table 2.3: 8 Features of IoT, Its Threat, Challenge, and Opportunity
Feature Threat Challenge Opportunity
Diversity Insecure protocols
Fragmented Dynamic analysis simulation platform, IDS
Myriad IoT botnet, DDoS
Intrusion detection and prevention
IDS
Intimacy Privacy leak Privacy protection Homomorphic encryption, Anonymous protocols Constrained Insecure
systems
Lightweight
defences and
protocols
Combining biological and physical characteristics Inter-
dependence
Bypassing static defences, Overprivileged
Access control and privilege
management
Context-based permission Ubiquitous Insecure
configuration
Safety
consciousness
Mobile Malware
propagation
Cross-domain identification and trust
Dynamic configuration
Each of those has the risks, research obstacles, and opportunities that each feature brings (Wei et al., 2019). In conclusion, they also demonstrate that various research and academic community security solutions have been offered to provide preventive, analytical, reactive, and corrective measures in Table 2.4 (Iqbal et al., 2020).
Table 2.4: Summaries of solutions proposed by researchers’ community
Summary
Dos/DDoS security solution
Data and Communication security solutions Privacy solutions
Anomaly detection solutions General security solutions 2.2.1 Security Challenges
IoT must be designed in a way where users may be certain their communications are conducted in a secure environment. The challenges that should be addressed in IoT research as depicted in Fig 2.1.
Figure 2.1: Addressing challenges in IoT research
IoT Environment
When considering the Internet of Things atmosphere, it is critical to keep in mind that safeguarding from foreign risks and attacks is just as critical as in a traditional I.C.T. environment.
The benefits of embracing IoT exceed the risks, according to 94 percent of retailers (Jovanovic, 2022).
In business, IoT is the critical component that provides intelligent devices to regulate homes automatically, in real-time view of the systems work. It provides information on just about everything the machine could provide or intended to do (Monjur, 2020).
While the Internet of Things is enormously effective by itself, it unlocks far greater potential whenever integrated with other technologies such as cloud computing, artificial intelligence, machine learning, big data, augmented and virtual reality, and cloud and edge computing (Oracle Corporation, 2018).
2.3.1 Platform fragmentation
Wide and varied innovations cause difficulties while planning and building multiple applications and services, restricting the ability to reuse data, particularly applications (such as architectures, baseband, APIs, and user interfaces) (Aly et al., 2018). Wireless communication for IoT devices, for example, can be achieved using Cat M1, NB-IoT, Z-Wave, LoRa, Zigbee, Bluetooth, and specialized proprietary frequencies — with its own set of benefits and drawbacks; and a distinct service environment (Al-Sarawi et al., 2017). There are also security concerns about flaws discovered in the core operating system, which are not frequently communicated to consumers of older, lower-cost gadgets, owing to the IoT's flexible computing architecture.
2.3.2 Privacy, autonomy, and control
The IoT promises citizen empowerment, government accountability, and knowledge exchange. At the same time, the risks to privacy and the possibilities for control of the masses and subterfuge are immense. Today, Internet users are fearful of disclosing details online and filling up application forms with fictitious addresses and name credentials (P.B Pankajavalli, 2018). The concept of privacy changes depending on one's ethnic background. Throughout time, the concept of privacy has evolved
and developed. Surveillance camera installation was once considered an invasive technique, but it is now commonplace. Deployment of sensors and a certain connection mechanism is required for applications or to render services in an organization.
3. Methodology
Introduction
The research will be conducted by literature review. This research before has identified the following current questions in this research.
3.1.1 Research objectives
• RO 1: Where does the Internet of Things stand in terms of security and privacy?
• RO 2: What is the state of IoT security and privacy in the industries that are utilizing it?
• RO 3: What are the industries' most pressing concerns about IoT security and privacy?
• RO 4: How does the existing level of security and privacy contribute to the IoT ecosystem's growth?
3.1.2 Research questions
• The following is the rationale for conducting this study:
• RQ 1: Examine the most recent IoT devices as well as security and privacy upgrades to perform the responsibilities effectively and efficiently.
• RQ 2: Examine the present state of IoT security and privacy.
• RQ 3: To look into the most pressing concerns about IoT security and privacy in industrial environments.
• RQ 4: To investigate the variables that contribute to the expansion of the IoT ecosystem in terms of security and privacy.
3.2 Systematic Review
In this area of study, we adopt the systematic literature review (SLR) guide by Yu Xiao, and Maria Watson (Xiao & Watson, 2017), depicted in Fig.3.1.
Figure 3.1 shows a step-by-step procedure for performing a literature review.
•Step 1 - Problem formulating
•Step 2 - Develope and validate protocol
Review Planning
•Step 3 - Literature search
•Step 4 - Inclusion screening
•Step 5 - Quality assessment
•Step 6 - Data extracting
•Step 7 - Data analyze and synthesize
Review
conducting •Step 8 - Report findings
Review reporting
Research Project Timeline
Time Stages
Week
1- 5 6 7-8 9 10 11 11-12
Research topic selection / /
Research topic introduction / /
Literature review / /
Primary data collection / /
Data analysis /
Discussion of research findings / /
Thesis writing / /
4. Findings
This series of tables seeks to summarize several IoT communication methods to answer RQ1 and RQ2. Additionally, it will evaluate widely-used IoT communication protocols, including the primary characteristics and behaviors of important indicators of power consumption, security, data dispersion, and other features. This comparison is meant to help researchers figure out which method is best for different situations.
Table 4.1 Standard Communication for IoT Protocols (source by (Al-Sarawi et al., 2017))
Characteristics Cellular
Bluetooth Low Energy
(LE)
IPv6 over Low-Power
Wireless Personal Area
Networks (6LoWPAN)
Near Field Communication
(NFC)
Radio Frequency Identification
(RFID)
Z-Wave SigFox ZigBee
Standard
3GPP and GSMA,
GSM / GPRS / EDGE (2G),
UMTS / HSPA (3G),
LTE (4G), 5G
IEEE
802.15.1 IEEE 802.15.4
ISO/IEC 14443 A&B,JIS X-
6319- 4
RFID Z-Wave SigFox IEEE802.15.
4
Frequency Bands
Common Cellular
bands
2.4 GHz
868Mhz(EU) 915Mhz(USA) 2.4Ghz(Global)
125Khz 13.56Mhz
860Mhz
125 kHz 13.56 MHz 902-928 MHz
868 MHz - 908 MHz
868MHz (EU)
902MHz(USA) 2.4 GHz
Network WNAN WPAN WPAN P2P Network Proximity WPAN LPWAN WPAN
Topology NA Star –Bus Network
Star Mesh
Network P2P Network P2P Network Mesh
Network Start Network
Star, Mesh Cluster Network
Power High power consumption
30 mA, Low Power
(1-2 years lifetime on batteries) Low
power consumption
50 mA, low power Very
Low
Ultra-low- power
2.5 mA Low power consumption
10 mW - 100 mW
30 mA Low power
Data Rate NA 1Mbps 250 kbps 106, 212, or 424
kbps 4 Mbps 40kbps 100 bps(UL),
600 bps(DL) 250 kbps
Range Several km Short Range
~15-30 m
Short Range 10-100 m
Short Range 0- 10cm, 0-1m,
10cm-1m
Short Range Up to 200 m
30m (indoors) 100(outdoors)
Long Range 10km (URBAN),
50km (RURAL)
Short Range 10-100 m
Security RC4 E0, Stream
AES-128 AES RSA AES RC4 AES-128 Partially
addressed AES
Features Longer Range
Low power version available
Commonly Used Internal Access
Security Low Cost Simple Protocol
Long Battery life (up to 20
years) Low Cost
Mesh Network
Common
Applications M2M
Wireless headsets, Audio Applications
Monitor and Control via
internet
Payment, Access
Tracking, Inventory, Access
Home Monitoring and Control
Street Lighting Energy meters
Home industry monitoring
and controlling
The following Table 4.13 details some of the most recent articles on countermeasures for implementing suggested security and privacy criteria, as well as limiting potential attacks on edge nodes.
Table 4.1: Implementation techniques for security and privacy Implementation
Techniques Researcher Article Title
Hardware Trojan Detection
(Sidhu et al., 2019) Hardware security in IoT devices with emphasis on hardware trojans (Guo et al., 2020) Securing IoT Space via Hardware Trojan Detection
(Dong, He, et al., 2019) A multi-layer hardware trojan protection framework for IoT chips (Chen et al., 2019) Toward FPGA Security in IoT: A New Detection Technique for Hardware Trojans (Dong, Chen, et al.,
2019)
A machine-learning-based hardware-Trojan detection approach for chips in the Internet of Things
Malicious Firmware Detection
(Anthi et al., 2019) A Supervised Intrusion Detection System for Smart Home IoT Devices
(Hafeez et al., 2020) IoT-KEEPER: Detecting Malicious IoT Network Activity Using Online Traffic Analysis at the Edge
(Banerjee et al., 2018) Blockchain-based security layer for identification and isolation of malicious things in IoT:
A conceptual design
(Seshadri et al., 2021) IoTCop: A Blockchain-Based Monitoring Framework for Detection and Isolation of Malicious Devices in Internet-of-Things System
Encryption
(Anthi et al., 2019) A Supervised Intrusion Detection System for Smart Home IoT Devices (Saleh et al., 2022) Proposing Encryption Selection Model for IoT Devices Based on IoT Device Design (Shen et al., 2019) Privacy-Preserving Support Vector Machine Training over Blockchain-Based Encrypted
IoT Data in Smart Cities (Bhandari &
Kirubanand, 2019) Enhanced encryption technique for secure IoT data transmission
Hash-based technique
(Seok et al., 2019) A Lightweight Hash-Based Blockchain Architecture for Industrial IoT
(Namanya et al., 2020) Similarity hash-based scoring of portable executable files for efficient malware detection in IoT
(Feroz Khan &
Anandharaj, 2021)
AHKM: An improved class of hash based key management mechanism with a combined solution for single hop and multi hop nodes in IoT
(Sharma et al., 2019) Secure Hash Authentication in IoT based Applications Lightweight
protocols
(Taresh, 2018) Lightweight protocols
(Kumar et al., 2020) A Lightweight Signcryption Method for Perception Layer in Internet-of-Things (Aman et al., 2021) A Lightweight Protocol for Secure Data Provenance in the Internet of Things Using
Wireless Fingerprints
Integrating PUF into the circuit
(Amsaad, Oun, et al., 2021)
Enhancing the Performance of Lightweight Configurable PUF for Robust IoT Hardware- Assisted Security
(Amsaad, Razaque, et
al., 2021) An Efficient and Reliable Lightweight PUF for IoT-based Applications (Lalouani et al., 2022) Countering Modeling Attacks in PUF-based IoT Security Solutions
(Balan et al., 2020) A PUF-based cryptographic security solution for IoT systems on chip Run-time attestation
(Kuang et al., 2020) DO-RA: Data-oriented runtime attestation for IoT devices (Ahmed et al., 2018) Program-flow attestation of IoT systems software
(Ankergård et al., 2021) State-of-the-art software-based remote attestation: Opportunities and open issues for the internet of things
Intrusion Detection system
(Eskandari et al., 2020) Passban IDS: An Intelligent Anomaly-Based Intrusion Detection System for IoT Edge Devices
(Dat-Thinh et al., 2022) MidSiot: A Multistage Intrusion Detection System for Internet of Things (Bakhsh et al., 2019) An adaptive intrusion detection and prevention system for the Internet of Things (Sai Kiran et al., 2020) Building an Intrusion Detection System for IoT Environment using Machine Learning
Techniques
Table 4.14 describes recent articles on pressing concerns about IoT security and privacy in industrial environments to answer RQ3.
Table 4.12: Recent research articles on major security issues Existing Work
Major Security Issues Identification Authentication Data
management Heterogeneity A Review of Identity Methods of Internet of Things (IoT)
(Bkheet et al., 2021) / /
Identifying IoT Devices Based on Spatial and Temporal
Features from Network Traffic(Yin et al., 2021) / / Privacy and Security Challenges and Solutions in IoT: A
review (Alhalafi & Veeraraghavan, 2019) / / / /
Security and Privacy Issues in IoT Environment
(Thilakarathne, 2020) / / / /
Security trends in Internet of Things: a survey (Bhatt & Rao
Ragiri, 2021) / / / /
A review for IoT authentication – Current research trends and
open challenges (Mehta & Patel, 2020) /
An Enhanced Lightweight IoT-based Authentication Scheme in Cloud Computing Circumstances (Martínez-Peláez et al., 2019)
/ /
New Method of Prime Factorisation-Based Attacks on RSA
Authentication in IoT (Venkatraman & Overmars, 2019) / IoT Devices, User Authentication, and Data Management in a
Secure, Validated Manner through the Blockchain System (Ahsan et al., 2022)
/ /
Efficient and Flexible Multi-Factor Authentication Protocol Based on Fuzzy Extractor of Administrator's Fingerprint and Smart Mobile Device (Mohammed & Yassin, 2019)
/ Internet of Things Security: Challenges and Key Issues
(Azrour et al., 2021) / / / /
Strategies to handle heterogeneity prevalent within an IOT
based network (Pavithra & Sastry, 2018) / /
Study of the heterogeneity problem in the Internet of Things
and Cloud Computing integration (Mafamane et al., 2021) / /
Security Challenges and Countermeasures for the
Heterogeneity of IoT Applications(Choudhary, 2019) /
Addressing Future Data Management Challenges in IoT: A
Proposed Framework (Asad et al., 2017) / /
Challenges and Research Issues of Data Management in IoT
for Large-Scale Petrochemical Plants (Shu et al., 2018) / /
IoT Data Quality Issues and Potential Solutions: A Literature
Review (Mansouri et al., 2021) /
IoT Data Management Using Cloud Computing and Big Data
Technologies (Gupta & Godavarti, 2020) /
Table 4.13 introduces recent works on guidelines to improve and sustain the expansion of the IoT ecosystem in terms of security and privacy, answering to RQ4
Table 4.13: Recent guidelines proposed addressing IoT features to improve
Addressed Features
Existing Work (Matsumoto
et al., 2021)
(Carman Ka Man et al.,
2015)
(Kandasamy et al., 2020)
(Dharshini &
Professor, 2019)
(Goworko &
Wytr˛, 2021) (Li et al., 2018) IoT Asset
Guidelines
Computing nodes / / /
Protocols / / / / /
Types of Guidelines
Privacy / / / / /
Security / / / /
Guidelines Intended for
Manufacturer / / / / /
Developer / / / / / /
Customer / / / /
5. Discussion
In this section, we discuss the lessons we learned from our approach to describe fundamental security issues of IoT networks and identify several initiatives for future work.
5.1. Referring to RQ1 and RQ 2
i. Node deployment - The topography of the wireless IoT network is constantly evolving.
Interruption from wireless signals is another substantial factor that could influence the edge node and functionality, for example, a poor GSM/GPRS signal.
ii. Disparate devices - The network's hubs are heterogeneous in terms of setup. The devices also differ in terms of resource consumption and vulnerability. It is harder to formulate a standard procedure capable of coping with heterogeneous devices. Interoperability, the burst of big data, and security are all factors to consider when designing an application to support heterogeneity.
iii. Network Access - IoT devices are interconnected on an intermittent or ad hoc basis in terms of power generation, which is a resource constraint for IoT nodes. Other significant connectivity issues include the undertaking of a unique identifier, effectiveness, scalability, and bandwidth utilization.
iv. Electricity Consumption - The majority of IoT devices are battery-powered, making them ideal for short-range communication. Intercommunication, which is needed for long-range heterogeneous devices, is one of the primary issues with IoT networks when it comes to transferring data across the network.
v. Fault tolerance - External conditions, rollout mechanisms, and limited battery life all affect network efficiency. In this, IoT nodes may fail or become inoperable for a range of reasons.
vi. Context-awareness - Routing is critical for accumulating environmental data, analyzing it, and spreading it. Prior work is used in a context-insensitive manner to provide safety in a closed environment, relying on node variables such as remaining energy, memory, computing power, and signal strength.
5.2. Referring to RQ3
Because the Internet of Things is comprised of reach large devices like sensors and RFID tags, it is critical to acclimate these devices to function in a traditional internet environment. As a result, it's difficult to use cryptographic algorithms, that frequently require that many resources than the tiny devices cohesively possess. Another difficulty is maintaining devices in the field. The accelerated growth of miniature embedded networks necessitates the development of efficient cryptographic techniques.
Due to the limited resources available with IoT, the scalability issue is exacerbated. Cryptographic systems in use even include significant amounts of energy, computing power, and memory. These functionalities are not always present in embedded objects. To this end, several research proposals have demonstrated that, due to its low resource consumption, elliptic curve cryptography could be used as a highly secured technique.
5.3. Referring to RQ4
The government's and tech firms' reaction to the COVID-19 outbreak has already sparked worries well about the privacy implications of using interaction tracing apps after and during the pandemic.
There seems to be a dispute between both the need for access control to improve services and the need to safeguard confidentiality. People are at high risk as a result of the accumulation of information recorded from IoT devices, after they become more identifiable via the use of profiles, marking, and unauthorized processing, which may contravene data protection acts such as General Data Protection Regulation (GDPR), which necessitate full permission from clients. Underneath the IoT philosophy, organizations believe in accumulating quite as much data as possible to obtain information and store them for a long period. In hypothesis, further data should result in increased understanding and advantage to organizations and society as a whole. As a result, imposing data mitigation will have a detrimental impact on the achievement of certain IoT applications.
Reference
Abdul-Ghani, H. A., & Konstantas, D. (2019). A comprehensive study of security and privacy guidelines, threats, and countermeasures: An IoT perspective. In Journal of Sensor and Actuator Networks (Vol. 8, Issue 2). MDPI AG. https://doi.org/10.3390/jsan8020022 Abeer, A., & Haya, A. (2018). IoT Security and Privacy Issues. 2018 1st International Conference
on Computer Applications & Information Security (ICCAIS).
https://doi.org/10.1109/CAIS.2018.8442002
Al-Sarawi, S., Anbar, M., Alieyan, K., & Alzubaidi, M. (2017). Internet of Things (IoT)
Communication Protocols : Review. 2017 8th International Conference on Information Technology (ICIT) .
Aly, M., Khomh, F., Guéhéneuc, Y.-G., Washizaki, H., & Yacout, S. (2018). Is Fragmentation a Threat to the Success of the Internet of Things? http://arxiv.org/abs/1808.07355
Frustaci, M., Pace, P., Aloi, G., & Fortino, G. (2018). Evaluating critical security issues of the IoT world: Present and future challenges. IEEE Internet of Things Journal, 5(4), 2483–2495.
https://doi.org/10.1109/JIOT.2017.2767291
Iqbal, W., Abbas, H., Daneshmand, M., Rauf, B., & Bangash, Y. A. (2020). An In-Depth Analysis of IoT Security Requirements, Challenges, and Their Countermeasures via Software- Defined Security. IEEE Internet of Things Journal, 7(10), 10250–10276.
https://doi.org/10.1109/JIOT.2020.2997651
Monjur, M. M. (2020). Internet-of-Things (IoT) Security Threats: Attacks on Communication Interface. https://scholars.unh.edu/thesis/1388
Nasajpour, M., Seyedamin Pouriyeh, ·, Parizi, R. M., Dorodchi, · Mohsen, Valero, M., Arabnia, H.
R., Yang, C. C., Facelli, J. C., Buckeridge, D., Wang, F., & Pouriyeh, S. (2020). Internet of Things for Current COVID-19 and Future Pandemics: an Exploratory Study. Journal of Healthcare Informatics Research, 4, 325–364. https://doi.org/10.1007/s41666-020-00080- 6
Oracle Corporation. (2018). Transformational Technologies: Today How IoT, AI, and blockchain will revolutionize business. https://www.oracle.com/a/ocom/docs/transformational-tech- wp.pdf
P.B Pankajavalli. (2018). INTERNET OF THINGS (IoT) Technologies, Applications, Challenges, and Solutions.
Sethi, P., & Sarangi, S. R. (2017). Internet of Things: Architectures, Protocols, and Applications. In Journal of Electrical and Computer Engineering (Vol. 2017). Hindawi Publishing
Corporation. https://doi.org/10.1155/2017/9324035
Weyrich, M., & Ebert, C. (2016). SOFTWARE TECHNOLOGY Reference Architectures for the Internet of Things. https://doi.org/10.1109/ms.2016.20
Goyal, S.B., Bedi, P., Kumar, J., Ankita (2022). Realtime Accident Detection and Alarm Generation System Over IoT. In: Kumar, R., Sharma, R., Pattnaik, P.K. (eds) Multimedia
Technologies in the Internet of Things Environment, Volume 2. Studies in Big Data, vol 93. Springer, Singapore. https://doi.org/10.1007/978-981-16-3828-2_6
Rajawat, A.S., Bedi, P., Goyal, S.B., Shaw, R.N., Ghosh, A. (2022). Reliability Analysis in Cyber- Physical System Using Deep Learning for Smart Cities Industrial IoT Network Node. In:
Piuri, V., Shaw, R.N., Ghosh, A., Islam, R. (eds) AI and IoT for Smart City Applications.
Studies in Computational Intelligence, vol 1002. Springer, Singapore.
https://doi.org/10.1007/978-981-16-7498-3_10
