Dependability and Security of IoT Network

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 10715

Special Issue Editors


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Guest Editor
Konkuk Aerospace Design-Airworthiness Research Institute (KADA), Konkuk University, Seoul 05029, Republic of Korea
Interests: dependability; security; IoT; quality of services; moving target defense; unmanned aerial mobility; mechatronics
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Guest Editor
Laboratory of Applied Research to Distributed Systems (PASID), Federal University of Piauí (UFPI), Picos 64600-000, Brazil
Interests: distributed systems; cloud/fog/edge computing; mobile computing; performance evaluation of systems

Special Issue Information

Dear Colleagues,

The internet of things (IoT) emerged as a general umbrella of connected technologies with massive data transactions generated by a vast number of heterogeneous things. IoT networks have been developed and adopted in a vast number of ecosystems, such as the internet of medical things (IoMT) in the field of medical healthcare, the internet of industrial things (IoIT) in industrial factories, the internet of robotic things (IoRT) in mechatronics, the internet of vehicle things (IoVT) in transportation, (which are abbreviated as Io*T), etc. Modern Io*T networks are constructed on top of a sophisticated and multi-level infrastructure of heterogeneous hardware/software systems, subsystems, and underlying components. Furthermore, the computing power and storage capabilities of IoT networks often rely on well-known computing backbones, including cloud/fog/edge computing paradigms, while sensing and data gathering rely on pervasiveness and ubiquity of sensors/devices at the very edge of the overall network. The IoT infrastructures, in practice, are strictly required with: 1) hosting latency-sensitive and likely real-time applications and services across the infrastructure; 2) a huge volume of data at a high-level of heterogeneity generated by IoT sensors/devices are seamlessly synchronized, stored, and processed across the infrastructure often at a high data transaction rate; and 3) a high level of dependability, and security measures are strictly required to satisfy a high level of data accuracy, economically operational costs, and user expectations for all operations at all levels of the IoT infrastructure.

Dependability and security are of five distinctive natures (along with functionality, performance, and cost) for computing and communication systems. Without exceptions, IoT networks with a sophisticated composition of multi-level systems and things are inevitably prone to a chain of threats (faults, errors, and failures) which eventually causes fatal losses, such as service interruption/outage, data leak, or even human lives. Even a 1% failure rate is too high, because it causes 3.65 days of unscheduled downtime in a year which, in turn, may reduce an enormous amount of enterprise turnover. Therefore, dependability and security requirements should be taken into consideration to obtain the highest level of trustworthiness for IoT-based infrastructures, in practice.

This Special Issue targets scientific contributions tackling these issues. Topics of interest include but are not limited to:

Keywords:

- IoT’s dependability and security modelling and evaluation;

- IoT’s dependability and security practice and solutions;

- Quality of services (QoS) for IoT networks;

- Fault-tolerant, disaster-tolerant IoT networks;

- Dependable artificial intelligence for IoT networks;

- Simulation and analytics of IoT networks;

- Cloud/Fog/Edge continuum in IoT networks;

- Io*T networks: IoMT, IoIT, IoRT, IoVT, etc.;

- IoT-based systems and networks for Unmanned Aerial Mobility (UAM);

- IoT networks in Industry 4.0, Digital Twin and Metaverse.

Dr. Tuan Anh Nguyen
Dr. Francisco Airton Silva
Guest Editors

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Published Papers (7 papers)

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Research

16 pages, 951 KiB  
Article
Token-Revocation Access Control to Cloud-Hosted Energy Optimization Utility for Environmental Sustainability
by Khaled Riad
Appl. Sci. 2023, 13(5), 3142; https://doi.org/10.3390/app13053142 - 28 Feb 2023
Viewed by 925
Abstract
To increase the usage of renewable energy, it is vital to maximize local energy production by properly combining various renewable-energy sources by collecting their data and storing it on the cloud. The energy optimization utility, which is used for making decisions to optimize [...] Read more.
To increase the usage of renewable energy, it is vital to maximize local energy production by properly combining various renewable-energy sources by collecting their data and storing it on the cloud. The energy optimization utility, which is used for making decisions to optimize renewable-energy resources, is hosted on the cloud to benefit from cloud capabilities in data storage. Hosting such sensitive data and utilities on the cloud has created some cybersecurity challenges. This paper presents a new token-revocation access control (TR-AC) which revokes the authorization of malicious users before authorizing them to access cloud-hosted energy optimization utilities. TR-AC employs a set of multi-authorities to measure the authentic level for each authenticated user. Although the user is authenticated to access the online system, this authentication can be revoked to utilize the energy optimization utility based on the user’s level of authentication. The cloud storage servers are not fully trusted and, therefore, have no control over access controls. Finally, the proposed TR-AC has been proven to be secure against any attacker that is not authentic according to Diffie-Hellman assumptions. In addition, performance analysis has proven that the time elapsed for both encryption and decryption in TR-AC is very small compared with previously introduced schemes. Therefore, it will not affect the performance of the cloud-hosted system. Full article
(This article belongs to the Special Issue Dependability and Security of IoT Network)
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24 pages, 1781 KiB  
Article
Performability Evaluation and Sensitivity Analysis of a Video Streaming on Demand Architecture
by Rubenilson de Sousa, Leonardo Cristian, Leonel Feitosa, Eunmi Choi, Tuan Anh Nguyen, Dugki Min and Francisco Airton Silva
Appl. Sci. 2023, 13(2), 998; https://doi.org/10.3390/app13020998 - 11 Jan 2023
Viewed by 1477
Abstract
In urban air mobility (UAM), video streaming platforms have gained significant attention from media companies due to their growing necessity for on-demand video streaming services-as-you-go in flights. Video streaming services can provide constant data transactions in a huge amount, especially in its operational [...] Read more.
In urban air mobility (UAM), video streaming platforms have gained significant attention from media companies due to their growing necessity for on-demand video streaming services-as-you-go in flights. Video streaming services can provide constant data transactions in a huge amount, especially in its operational digital twin (ODT). As a result, the ability to provide a satisfactory user experience through video streaming platforms is critical and complex. This requires continuously operating services while handling numerous user requests for near real-time video streaming. At the same time, high-quality video with high resolution and minimal interruptions is often expected. Therefore, the availability and performance (i.e., performability) of the Back-End video streaming infrastructure are crucial parameters for these platforms. However, evaluating novel video-on-demand architectures in real-world scenarios can be costly due to the numerous parameters involved. Analytical models, such as stochastic Petri nets (SPNs), can serve as an alternative in this complex scenario as they can be used to analyze systems during the design process. In this study, we developed a set of SPN models to assess the performance of a video-on-demand system. These models were designed to illustrate and to evaluate a video-on-demand architecture while considering performance. We had a base SPN model as well as three enhanced variants available. The extended models were generated using the Design of Experiments (DoE) technique and sensitivity analysis results. The DoE identified the factors with the greatest impacts on performance, and the most significant factor interactions. Redundancy strategies were applied to the extended models to increase the availability of the most important components. This redundancy increased the availability of “9 s” from three to five. It is worth noting that this study can help the designers of video streaming systems, to plan and to optimize their ideas based on the provided models. Full article
(This article belongs to the Special Issue Dependability and Security of IoT Network)
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17 pages, 5675 KiB  
Article
Application of an Internet of Medical Things (IoMT) to Communications in a Hospital Environment
by Boseul Kim, Sunghae Kim, Min Lee, Hyukjae Chang, Eunjeong Park and Taehwa Han
Appl. Sci. 2022, 12(23), 12042; https://doi.org/10.3390/app122312042 - 25 Nov 2022
Cited by 7 | Viewed by 2174
Abstract
IoT technology is used in various industries, including the manufacturing, energy, finance, education, transportation, smart home, and medical fields. In the medical field, IoT applications can provide high-quality medical services through the efficient management of patients and mobile assets in hospitals. In this [...] Read more.
IoT technology is used in various industries, including the manufacturing, energy, finance, education, transportation, smart home, and medical fields. In the medical field, IoT applications can provide high-quality medical services through the efficient management of patients and mobile assets in hospitals. In this paper, we introduce an IoT system to the medical field using Sigfox, a low-power communication network for indoor location monitoring used as a hospital network. A proof-of-concept (PoC) was implemented to evaluate the effectiveness of medical device and patient safety management. Specific requirements should be considered when applying the IoMT system in a hospital environment. In this study, the location and temperature of various targets sending signals to the monitoring system using three different networks (Sigfox, Hospital and Non-Hospital) were collected and compared with true data, the average accuracy of which were 69.2%, 72.5%, and 83.3%, respectively. This paper shows the significance in the application of an IoMT using the Sigfox network in a hospital setting in Korea compared with existing hospital networks. Full article
(This article belongs to the Special Issue Dependability and Security of IoT Network)
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14 pages, 3635 KiB  
Article
Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection in Online Social Networks
by Dalia H. Elkamchouchi, Jaber S. Alzahrani, Mashael M. Asiri, Mesfer Al Duhayyim, Heba Mohsen, Abdelwahed Motwakel, Abu Sarwar Zamani and Ishfaq Yaseen
Appl. Sci. 2022, 12(14), 7119; https://doi.org/10.3390/app12147119 - 14 Jul 2022
Cited by 3 | Viewed by 1413
Abstract
Sarcasm detection has received considerable interest in online social media networks due to the dramatic expansion in Internet usage. Sarcasm is a linguistic expression of dislikes or negative emotions by using overstated language constructs. Recently, detecting sarcastic posts on social networking platforms has [...] Read more.
Sarcasm detection has received considerable interest in online social media networks due to the dramatic expansion in Internet usage. Sarcasm is a linguistic expression of dislikes or negative emotions by using overstated language constructs. Recently, detecting sarcastic posts on social networking platforms has gained popularity, especially since sarcastic comments in the form of tweets typically involve positive words that describe undesirable or negative characteristics. Simultaneously, the emergence of machine learning (ML) algorithms has made it easier to design efficacious sarcasm detection techniques. This study introduces a new Hosted Cuckoo Optimization Algorithm with Stacked Autoencoder-Enabled Sarcasm Detection and Classification (HCOA-SACDC) model. The presented HCOA-SACDC model predominantly focuses on the detection and classification of sarcasm in the OSN environment. To achieve this, the HCOA-SACDC model pre-processes input data to make them compatible for further processing. Furthermore, the term frequency-inverse document frequency (TF-IDF) model is employed for the useful extraction of features. Moreover, the stacked autoencoder (SAE) model is utilized for the recognition and categorization of sarcasm. Since the parameters related to the SAE model considerably affect the overall classification performance, the HCO algorithm is exploited to fine-tune the parameters involved in the SAE, showing the novelty of the work. A comprehensive experimental analysis of a benchmark dataset is performed to highlight the superior outcomes of the HCOA-SACDC model. The simulation results indicate that the HCOA-SACDC model accomplished enhanced performance over other techniques. Full article
(This article belongs to the Special Issue Dependability and Security of IoT Network)
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17 pages, 2265 KiB  
Article
Situation-Aware Survivable Network Design for Tactical Environments
by Sunghwa Son, Gwangjin Wi and Kyung-Joon Park
Appl. Sci. 2022, 12(13), 6738; https://doi.org/10.3390/app12136738 - 03 Jul 2022
Cited by 1 | Viewed by 1176
Abstract
A tactical sensor network is a representative safety-critical environment that should satisfy strict guarantee of the requirements of tactical traffic. However, because of the lack of infrastructure in a military network environment, resource constraints on wireless channel and nodes can cause problems such [...] Read more.
A tactical sensor network is a representative safety-critical environment that should satisfy strict guarantee of the requirements of tactical traffic. However, because of the lack of infrastructure in a military network environment, resource constraints on wireless channel and nodes can cause problems such as network congestion and packet collision. If critical tactical data is lost or does not arrive on time, it can degrade the efficiency of military operations and even threaten the survival of soldiers. To resolve this critical issue, we propose a situational backoff reset algorithm that utilizes a quality of service (QoS) field information to determine the priority of received tactical packets and control the deferral time of low-priority traffic. From a packet routing path connectivity perspective, we propose a branch node-based routing algorithm in order to provide a resilient path by excluding the isolated single path. Our simulation results demonstrate that the proposed solution can prioritize tactical traffic from the channel preemption perspective and construct a robust end-to-end path avoiding an isolated single path. Full article
(This article belongs to the Special Issue Dependability and Security of IoT Network)
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18 pages, 4952 KiB  
Article
hLSTM-Aging: A Hybrid LSTM Model for Software Aging Forecast
by Felipe Battisti, Arnaldo Silva, Luis Pereira, Tiago Carvalho, Jean Araujo, Eunmi Choi, Tuan Anh Nguyen and Dugki Min
Appl. Sci. 2022, 12(13), 6412; https://doi.org/10.3390/app12136412 - 24 Jun 2022
Viewed by 1460
Abstract
Long-running software, such as cloud computing services, is now widely used in modern applications. As a result, the demand for high availability and performance has grown. However, these applications are more vulnerable to software aging issues and are more likely to fail due [...] Read more.
Long-running software, such as cloud computing services, is now widely used in modern applications. As a result, the demand for high availability and performance has grown. However, these applications are more vulnerable to software aging issues and are more likely to fail due to the accumulation of mistakes in the system. One popular strategy for dealing with such aging-related problems is to plan prediction-based software rejuvenation activities based on previously obtained data from long-running software. Prediction algorithms enable the activation of a mitigation mechanism before the problem occurs. The long short-term memory (LSTM) neural network, the present state of the art in temporal series prediction, has demonstrated promising results when applied to software aging concerns. This study aims to anticipate software aging failures using a hybrid prediction model integrating long short-term memory models and statistical approaches. We emphasize the capabilities of each strategy in various long-running software scenarios and provide an untried hybrid model (hLSTM-aging) based on the union of Conv-LSTM networks and probabilistic methodologies, attempting to combine the strengths of both approaches for software aging forecasts. The hLSTM-aging prediction results revealed how hybrid models are a compelling solution for software-aging prediction. Experiments showed that hLSTM-aging increased MSE criteria by 8.54% to 50% and MAE criteria by 3.53% to 14.29% when compared to Conv-LSTM, boosting the model’s initial performance. Full article
(This article belongs to the Special Issue Dependability and Security of IoT Network)
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12 pages, 2909 KiB  
Article
A New Model for a Secure Social Media Application
by Khaled Riad and Mohamed Elhoseny
Appl. Sci. 2022, 12(13), 6375; https://doi.org/10.3390/app12136375 - 23 Jun 2022
Cited by 1 | Viewed by 1237
Abstract
The progress of data technology and wireless networks is generated by open online communication channels. Unfortunately, trolls are abusing the technology for executing cyberattacks and threats. An automated cybersecurity solution is vital for avoiding the threats and security issues from social media. This [...] Read more.
The progress of data technology and wireless networks is generated by open online communication channels. Unfortunately, trolls are abusing the technology for executing cyberattacks and threats. An automated cybersecurity solution is vital for avoiding the threats and security issues from social media. This can be a requirement for tackling and considering cyberbullying in various aspects including prevention of such incidents and automated detection. This study introduces a novel Artificial Fish Swarm Algorithm with Weighted Extreme Learning Machine (AFSA-WELM) model for cybersecurity on social media. The proposed model is mostly intended to detect the existence of cyberbullying on social media. The proposed model starts by processing the dataset and making it ready for the next stages of the model. It then uses the TF-IDF vectorizer for word embedding. After that, it uses the WELM model for the identification and classification of cyberbullying. Finally, the optimal tunning parameters used in the WELM model are derived for the AFSA model. The experimental analysis has shown that the proposed model achieves maximum accuracy compared with existing algorithms. Moreover, our proposed model achieves maximum precision–recall performance with various datasets. Full article
(This article belongs to the Special Issue Dependability and Security of IoT Network)
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