Publikační činnost Katedry telekomunikačních technologií / Publications of Department of Telecommunications (440)

Permanent URI for this collectionhttp://hdl.handle.net/10084/64796

Kolekce obsahuje bibliografické záznamy publikační činnosti (článků) akademických pracovníků Katedry telekomunikačních technologií (440) v časopisech registrovaných ve Web of Science od roku 2003 po současnost.
Do kolekce jsou zařazeny:
a) publikace, u nichž je v originálních dokumentech jako působiště autora (adresa) uvedena Vysoká škola báňská-Technická univerzita Ostrava (VŠB-TUO),
b) publikace, u nichž v originálních dokumentech není v adrese VŠB-TUO uvedena, ale autoři prokazatelně v době jejich zpracování a uveřejnění působili na VŠB-TUO.

Bibliografické záznamy byly původně vytvořeny v kolekci Publikační činnost akademických pracovníků VŠB-TUO, která sleduje publikování akademických pracovníků od roku 1990.

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Now showing 1 - 20 out of 374 results

DT-LSMAS: Digital Twin-Assisted Large-Scale Multiagent System for Healthcare Workflows
(IEEE, 2024) Lakhan, Abdullah; Mohammed, Mazin Abed; Zebar, Dilovan Asaad; Abdulkareem, Karrar Hameed; Deveci, Muhammet; Marhoon, Haydar Abdulameer; Nedoma, Jan; Martinek, Radek
Digital healthcare has garnered much attention from academia and industry for health and well-being. Many digital healthcare architectures based on large-scale edge and cloud multiagent systems (LSMASs) have recently been presented. The LSMAS allows agents from different institutions to work together to achieve healthcare processing goals for users. This article presents a digital twin large-scale multiagent strategy (DT-LSMAS) comprising mobile, edge, and cloud agents. The DT-LSMAS comprised different schemes for healthcare workflows, such as added healthcare workflows, application partitioning, and scheduling. We consider healthcare workflows with different biosensor data such as heartbeat, blood pressure, glucose monitoring, and other healthcare tasks. We partitioned workflows into mobile, edge, and cloud agents to meet the deadline, total time, and security of workflows in large-scale edge and cloud nodes. To handle the large-scale resource for real-time sensor data, we suggested digital twin-enabled edge nodes, where delay-sensitive workflow tasks are scheduled and executed under their quality of service requirements. Simulation results show that the DT-LSMAS outperformed in terms of total time by 50%, minimizing the risk of resource leakage and deadline missing during scheduling on heterogeneous nodes. In conclusion, the DT-LSMAS obtained optimal results for workflow applications.
Efficient handling of ACL policy change in SDN using reactive and proactive flow rule installation
(Springer Nature, 2024) Hussain, Mudassar; Amin, Rashid; Gantassi, Rahma, rahma; Alshehri, Asma Hassan; Frnda, Jaroslav; Raza, Syed Mohsan, Syed Mohsan
Software-defined networking (SDN) is a pioneering network paradigm that strategically decouples the control plane from the data and management planes, thereby streamlining network administration. SDN's centralized network management makes configuring access control list (ACL) policies easier, which is important as these policies frequently change due to network application needs and topology modifications. Consequently, this action may trigger modifications at the SDN controller. In response, the controller performs computational tasks to generate updated flow rules in accordance with modified ACL policies and installs flow rules at the data plane. Existing research has investigated reactive flow rules installation that changes in ACL policies result in packet violations and network inefficiencies. Network management becomes difficult due to deleting inconsistent flow rules and computing new flow rules per modified ACL policies. The proposed solution efficiently handles ACL policy change phenomena by automatically detecting ACL policy change and accordingly detecting and deleting inconsistent flow rules along with the caching at the controller and adding new flow rules at the data plane. A comprehensive analysis of both proactive and reactive mechanisms in SDN is carried out to achieve this. To facilitate the evaluation of these mechanisms, the ACL policies are modeled using a 5-tuple structure comprising Source, Destination, Protocol, Ports, and Action. The resulting policies are then translated into a policy implementation file and transmitted to the controller. Subsequently, the controller utilizes the network topology and the ACL policies to calculate the necessary flow rules and caches these flow rules in hash table in addition to installing them at the switches. The proposed solution is simulated in Mininet Emulator using a set of ACL policies, hosts, and switches. The results are presented by varying the ACL policy at different time instances, inter-packet delay and flow timeout value. The simulation results show that the reactive flow rule installation performs better than the proactive mechanism with respect to network throughput, packet violations, successful packet delivery, normalized overhead, policy change detection time and end-to-end delay. The proposed solution, designed to be directly used on SDN controllers that support the Pyretic language, provides a flexible and efficient approach for flow rule installation. The proposed mechanism can be employed to facilitate network administrators in implementing ACL policies. It may also be integrated with network monitoring and debugging tools to analyze the effectiveness of the policy change mechanism.
Multi-agent reinforcement learning framework based on information fusion biometric ticketing data in different public transport modes
(Elsevier, 2024) Lakhan, Abdullah; Rashid, Ahmed N.; Mohammed, Mazin Abed; Zebari, Dilovan Asaad; Deveci, Muhammet; Wang, Limin, limin; Abdulkareem, Karrar Hameed; Nedoma, Jan; Martinek, Radek
In smart cities, biometric technologies have become extensively used for ticket authentication on public transport. Information fusion plays a key role in biometric ticketing, allowing ticket validation with more data source validation in different public transport modes. This paper proposes a novel biometric technology -based mobile ticket application -based system. We formulate the problem as a multi -agent reinforcement learning framework for biometric ticketing in multi -transport environments. Specifically, we propose the Asynchronous Advantage Critic Biometric Ticketing Framework (A3CBTF) algorithm, which consists of different schemes based on the proposed system. The proposed algorithm framework operates in hybrid transport modes using a parallel reinforcement learning scheme. A key advantage of A3CBTF is that it enables passengers to use a single ticket for various public transport modes. Additionally, even when a passenger's mobile device is stolen, lost, or has a dead battery, they can still validate their tickets through different information fusion sources, such as fingerprint and face recognition. A3CBTF is a multi -agent system that integrates mobile, transport, edge, and cloud servers to facilitate ticket validation in a distributed environment. By optimizing both convex and concave optimizations, A3CBTF ensures efficient ticket validation with minimal processing time and maximizes validation rewards across different biometric technologies. Experimental results demonstrate that A3CBTF outperforms mobile off with other options such as fingerprint and face recognition in public transport as compared to other ticketing systems.
Renewable energy resource management using an integrated robust decision making model under entropy and similarity measures of fuzzy hypersoft set
(Elsevier, 2024) Saeed, Muhammad Haris; Saeed, Muhammad; Rahman, Atiqe Ur; Ahsan, Muhammad; Mohammed, Mazin Abed; Marhoon, Haydar Abdulameer; Nedoma, Jan; Martinek, Radek
The demand for renewable energy has significantly increased over the last decade with increased attention to the preservation of the environment and sustainable, optimal resource management. As traditional sources of energy production are depleting at an alarming rate and causing longlasting environmental damage, it is essential to explore green and cost-effective methodologies for meeting energy demand. With each country having different geographical, political, social, and natural factors, the problem arises of which renewable energy should be utilized for optimal resource management. This multi -criteria decision making (MCDM) challenge is tackled by applying a dynamic fuzzy hypersoft set -based Method for the evaluation of currently deployed Renewable Energy systems and providing a decision support system for the installation of new ones based on the factors mentioned above for Turkey. As the installation of new renewable energy projects and the evaluation of old ones is significantly influenced by human judgment, it leaves great room for uncertainty primarily because of the psychological factors of the expert. The novel concept of Fuzzy Hypersoft Sets (FHSs) and their Entropy (EN) and TOPSIS-based operations are first discussed with reference to the problem at hand. The presented structure is superior to the ones in the literature by allowing access to data parameters as sub -parametric values while utilizing the versatility of Fuzzy structures to deal with uncertainty. The technique has great potential to serve as a potential decision support system in any setting. For now, hypothetical expert ratings are used to illustrate the working of the dynamic structure along with a sensitivity analysis to investigate the primary criterion weights in sorting. The evaluation of currently deployed renewable energy systems using our methodology revealed an average improvement in system performance compared to traditional methods. Furthermore, the decision support system for the installation of new projects based on geographical, political, social, and natural factors exhibited a potential increase in overall system efficiency. These numeric outcomes highlight the effectiveness and practical applicability of our approach in optimizing resource management and fostering sustainable energy practices.
Load monitoring and appliance recognition using an inexpensive, low-frequency, data-to-image, neural network, and network mobility approach for domestic IoT systems
(IEEE, 2024) Fazio, Peppino; Mehić, Miralem; Vozňák, Miroslav
With the low integration costs and quick development cycle of all-IP-based 5G+ technologies, it is not surprising that the proliferation of IP devices for residential or industrial purposes is ubiquitous. Energy scheduling/management and automated device recognition are popular research areas in the engineering community, and much time and work have been invested in producing the systems required for smart city networks. However, most proposed approaches involve expensive and invasive equipment that produces huge volumes of data (high-frequency complexity) for analysis by supervised learning algorithms. In contrast to other studies in the literature, we propose an approach based on encoding consumption data into vehicular mobility and imaging systems to apply a simple convolutional neural network to recognize certain scenarios (devices powered on) in real time and based on the nonintrusive load monitoring paradigm. Our idea is based on a very cheap device and can be adapted at a very low cost for any real scenario. We have also created our own data set, taken from a real domestic environment, contrary to most existing works based on synthetic data. The results of the study's simulation demonstrate the effectiveness of this innovative and low-cost approach and its scalability in function of the number of considered appliances.
Construction of a high-temperature sensor for industry based on optical fibers and ruby crystal
(MDPI, 2024) Hercík, Radim; Mikolajek, Martin; Byrtus, Radek; Hejduk, Stanislav; Látal, Jan; Vanderka, Aleš; Macháček, Zdeněk; Koziorek, Jiří
This paper presents the construction of an innovative high-temperature sensor based on the optical principle. The sensor is designed especially for the measurement of exhaust gases with a temperature range of up to +850 degrees C. The methodology is based on two principles-luminescence and dark body radiation. The core of this study is the description of sensing element construction together with electronics and the system of photodiode dark current compensation. An advantage of this optical-based system is its immunity to strong magnetic fields. This study also discusses results achieved and further steps. The solution is covered by a European Patent.
Toward facilitating power efficient URLLC systems in UAV networks under jittering
(IEEE, 2024) Ranjha, Ali; Javed, Muhammad Awais; Piran, Md. Jalil; Asif, Muhammad; Hussien, Mostafa; Zeadally, Sherali; Frnda, Jaroslav
Consumer electronics can support sixth-generation (6G) systems and their services, including ultra-reliable and low-latency communications (URLLC). In this context, unmanned aerial vehicles (UAVs) have become increasingly popular because they can: be dynamically positioned, take advantage of channel gains, and communicate directly via line-of-sight. UAVs, on the other hand, are unable to maintain a stable flight for prolonged periods of time and suffer from jittering impairments caused by strong winds. As a result of atmospheric conditions and environmental interference, the perfect channel state information (CSI) becomes obsolete. The aim of this study is to propose a power-efficient resource allocation scheme for URLLC-enabled UAV communication systems under finite block lengths, imperfect CSIs, and adverse jittering effects caused by wind. This involves optimizing UAV positioning and blocklength distribution together. Additionally, we propose a perturbation-based semidefinite programming (SDP) approach to reduce the sum power and demonstrate that it outperforms fixed benchmark algorithms. As such, it can reach power savings up to 77.18% compared to fixed benchmark algorithms. Our extensive simulation results demonstrate that our approach performs similarly to the exhaustive search and has low complexity. Thus, the proposed method thus provides a practical power-efficient URLLC implementation for memory-constrained UAV networks.
Highly stabilized fiber Bragg grating accelerometer based on cross-type diaphragm
(Optica Publishing Group, 2024) Wei, Heming; Zhuang, Changquan; Che, Jiawei; Zhang, Dengwei; Zhu, Mengshi; Pang, Fufei; Caucheteur, Christophe; Hu, Xuehao; Nedoma, Jan; Martinek, Radek; Marques, Carlos
A fiber Bragg grating (FBG) accelerometer based on cross-type diaphragm was proposed and designed, in which the cross-beam acts as a spring element. To balance the sensitivity and stability, the accelerometer structure was optimized. The experimental results show that the designed device has a resonant frequency of 556 Hz with a considerable wide frequency bandwidth of up to 200 Hz, which is consistent with the simulation. The sensitivity of the device is 12.35 pm/g@100 Hz with a linear correlation coefficient of 0.99936. The proposed FBG accelerometer has simple structure and strong anti-interference capability with a maximal cross-error less than 3.26%, which can be used for mechanical structural health monitoring.
BEDS: Blockchain energy efficient IoE sensors data scheduling for smart home and vehicle applications
(Elsevier, 2024) Lakhan, Abdullah; Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; Deveci, Muhammet; Marhoon, Haydar Abdulameer; Memon, Sajida; Nedoma, Jan; Martinek, Radek
In the current period, there has been a prominent and gradual upswing in the application of Internet of Energy (IoE) sensors in smart cities. These sensors play a vital role across diverse aspects of the energy sector, ranging from producing energy to haggling with the complexities of the smart grid. The IoE sensors use fiber optics technology that increases the speed and bandwidth of data transfer in smart grid applications. Incorporating IoE sensors, including fiber optics, is paramount to rehabilitating extensive IoE sensor data into practical information to distribute energy based on prices, availability, and demand for smart homes and electric vehicles. However, from energy generation to consumption, many nodes are incorporated. Therefore, security is a crucial challenge to processing accurate IoE sensor data for energy generation and consumption in smart cities. This paper presents blockchain-enabled, energy-efficient IoE smart grid architecture for smart homes and electric vehicle applications. The proposed architecture suggests blockchain-energy-efficient IoE sensors data scheduling (BEDS) algorithm schemes that consist of blockchain, smart grid, and vehicle and smart home schemes. The paper considers grid data based on sensors for different applications. The proposed system integrates fiber optics, collecting and offloading sensors to the grid for execution. This study aims to process IoE sensor data based on blockchain with a minimum processing time of 29% and less power consumption of 41%. Simulation results show that BEDS has less processing time and energy consumption than existing proof-of-work and proof-of-stake blockchain methods in smart grid networks.
The role of smart optical biosensors and devices on predictive analytics for the future of aquaculture systems
(Elsevier, 2024) Soares, Maria Simone; Singh, Ragini; Kumar, Santosh; Jha, Rajan; Nedoma, Jan; Martinek, Radek; Marques, Carlos
Recirculating aquaculture systems (RAS) have been rising quickly in the last decade, representing a new way to farm fish with sustainable aquaculture practices. This system is an environmentally and economically sustainable technology for farming aquatic organisms by reusing the water in production. RAS present some benefits compared with other aquaculture methods, for instance, allows the minimization of water usage and disease occurrence, the absence of antibiotics in these systems, shortens the production cycle, functions as a water treatment system, allows the improvement of the feed conversion, and a reduction in the alteration of coastal habitat, among others. However, this is a complex system with complex interactions between the number of fish and water quality parameters, which can compromise the fish welfare. Currently, there is a huge gap in the global aquaculture sector in terms of smart sensors for cortisol (stress hormone), bacteria, water pollutants, volatile organic compounds and micro/nano-plastics assessment. This sector does not measure such critical parameters which brings a weak understanding of the wellbeing of fish. Therefore, it is crucial to implement point of care (POC) sensors for those critical parameters' assessment via multiparameter solution and predictive analytic capabilities for data supply. This work presents an overall introduction about the impact of the RAS on fish production and its necessity as protein as well as the actual solutions for those problems. Additionally, it reviews the actual state of the art in terms of potential multiparameter POC sensors and predictive analytical approaches that have been investigated in recent years for future application in aquaculture with the aim to guide the researchers on the sector's needs. Additionally, future perspectives are also described in order to digitize the aquaculture sector with novel optical systems and biosensing elements.
Outage probability analysis for relay-aided self-energy recycling wireless sensor networks over INID Rayleigh fading channels
(IEEE, 2024) Nguyen, Tan N.; Van Chien, Trinh; Dinh, Viet Quang; Tu, Lam-Thanh; Vozňák, Miroslav; Ding, Zhiguo
Communication reliability is one of the key challenging issues in future communications due to massive connections, especially for wireless sensor networks (WSNs) with low-cost devices. This article studies the communication reliability of wireless systems in the presence of multiple sensor relays, which carry out energy harvesting to prolong the network lifetime. By exploiting the deep shadow fading model, the three sensor selection methods are investigated based on the different prior information of the propagation channels. We then derive the analytical expressions of the outage probability (OP) for each sensor selection, which only depends on the statistical channel knowledge that can be applied for multiple coherence intervals whenever the channel statistics remain the same. Since the obtained analytical OPs are interpreted based on several coupled integrals that are costly to compute, we further propose a learning framework to predict the OP with low computational complexity via exploiting supervised learning. Numerical results indicate that the two suboptimal sensor selection solutions provide a competitive OP with each other. In contrast, the optimal solution outperforms the remaining benchmarks by many folds. Besides, the deep-learning-based approach performs almost the same performance as the analytical-based framework.
Modeling a neurological disorder as the result of an operator acting on the brain: A first sketch based on network channel modeling
(AIP Publishing, 2024) Mannone, Maria; Fazio, Peppino; Marwan, Norbert
The brain is a complex network, and diseases can alter its structures and connections between regions. Therefore, we can try to formalize the action of diseases by using operators acting on the brain network. Here, we propose a conceptual model of the brain, seen as a multilayer network, whose intra-lobe interactions are formalized as the diagonal blocks of an adjacency matrix. We propose a general and abstract definition of disease as an operator altering the weights of the connections between neural agglomerates, that is, the elements of the brain matrix. As models, we consider examples from three neurological disorders: epilepsy, Alzheimer-Perusini's disease, and schizophrenia. The alteration of neural connections can be seen as alterations of communication pathways, and thus, they can be described with a new channel model.
Holographic reconfigurable intelligent surface-aided downlink NOMA IoT networks in short-packet communication
(IEEE, 2024) Vo, Dinh-Tung; Nguyen, Tan N.; Le, Anh-Tu; Phan, Van-Duc; Vozňák, Miroslav
Non-orthogonal multiple access (NOMA) technology is projected to significantly increase the spectrum efficiency of the fifth-generation and subsequent wireless networks. Holographic reconfigurable Intelligent surfaces (HRISs) are a revolutionary technology that can deliver excellent spectral and energy efficiency at a cheap cost in wireless networks. In this letter, we investigate the short-packet communication (SPC) with the NOMA-based HRIS system with the internet of things (IoT). A base station (BS) communicates with two NOMA users by using HRIS in the proposed system to enhance spectral efficiency. Furthermore, we derived the exact closed-form expression of the average block error rate (BLER) for two NOMAusers. Toget more insight into the proposed system, the asymptotic BLER analysis was also carried out at high signal-to-noise ratio regime. The numerical results validate the current analysis and show that the presented NOMA strategy exceeds orthogonal multiple access-based approaches in terms of BLER and throughput.
Short packet communications for relay systems with co-channel interference at relay: Performance analysis and power control
(IEEE, 2024) Nguyen, Quang-Sang; Anh, Uyen-Vu Le; Nguyen, Tan N.; Nguyen, Tien-Tung; Vozňák, Miroslav
In this paper, we evaluate short packet communication (SPC) for a cooperative system where one relay assists data transmission between one multi-antenna source and one single antenna destination with the presence of co-channel interference at the relay. Two transmission schemes, i.e., transmit antenna selection (TAS) scheme and beamforming scheme (BF), are considered. Based on metric of SPC, we derive the average block error rate (BLER) for the system in asymptotic and closed-form expressions for both schemes. In addition, a solution of optimal power allocation (OPA) to maximize end-to-end effective system throughput is proposed. Effects of parameters such as total transmit power, number of the source’s antennas, numberofco-channelinterference,andpacketlengthontheperformanceofthesystemareevaluated.Finally, the results reveal that the performance of the OPA scheme outperforms that of the benchmark scheme, i.e., equal power allocation solution, in terms of BLER and the effective throughput for both the TAS and BFschemes. Moreover, the performance of the system reaches to saturation value with more antennas at the source. The findings indicate that the TAS and BF schemes have the same performance in terms of both the BLERand the effective throughput.
FBG sensor for heart rate monitoring using 3D printing technology
(IEEE, 2024) Fajkus, Marcel; Kostelanský, Michal; Fridrich, Michael; Čubík, Jakub; Kepák, Stanislav; Križan, Daniel; Martinek, Radek; Mohammed, Mazin Abed; Nedoma, Jan
Currently, the use of fiber-optic Bragg gratings in biomedical applications, especially in the field of magnetic resonance imaging (MRI), is becoming popular. In these applications, the fiber Bragg grating (FBG) encapsulation plays a crucial role in terms of the accuracy and reproducibility of the measurements. This paper describes in detail the fabrication method of a prototype FBG sensor, which is realized by encapsulating a Bragg grating between two layers of the MR-compatible material Acrylonitrile Butadiene Styrene (ABS) by 3D printing. The sensor thus created, implemented, for example, on the chest of a human body, enables monitoring of the vital functions of the human body. The paper describes the complete procedure for the creation of the prototype sensor, including strain and temperature dependence, as well as results of long-term experimental measurements against the conventional electrocardiography (ECG) standard. Results based on the objective Bland-Altman (B-A) method confirm that the implemented sensor can be used for reliable monitoring of cardiac activity (>95% based on B-A). Taking into account the single fiber optic cable, its simple implementation, its small size and weight < 5g, the presented sensor represents an interesting alternative to conventional ECG.
An SDN-enabled fog computing framework for wban applications in the healthcare sector
(Elsevier, 2024) Tripathy, Subhranshu Sekhar; Bebortta, Sujit; Mohammed, Mazin Abed; Nedoma, Jan; Martinek, Radek; Marhoon, Haydar Abdulameer
For healthcare systems utilizing Wireless Body Area Networks (WBANs), maintaining the network's diverse Quality of Service (QoS) metrics necessitates effective communication among Fog Computing resources. While fog nodes efficiently handle local requests with substantial processing resources, it is crucial to acknowledge the unpredictable availability of these nodes, potentially resulting in a decline in system performance. This study explores a software-defined fog architecture supporting different healthcare applications in Internet of Things (IoT) environment to ensure consistent specialized medical care amidst evolving health issues. Even minor delays, packet losses, or network overhead could adversely affect patient health. The article establishes a mathematical foundation based on transmitted and sensed data, ensuring each fog node executes an ideal quantity of processes. This study formulates an optimization problem to maximize the utility of fog nodes, leveraging the Lagrangian approach and Karush-Kuhn-Tucker conditions to streamline and resolve the optimization problem. Performance analysis demonstrates a significant reduction in delays by approximately 38 %, 29 %, and 32 %, along with energy savings of roughly 26.89 %, 12.16 %, and 22.50 %, compared to benchmark approaches. This study holds promise in healthcare, cloud-fog simulation, and WBANs, emphasizing the critical need for swift and accurate data processing.
Physical layer security analysis for RIS-aided NOMA systems with non-colluding eavesdroppers
(Elsevier, 2024) Le, Anh-Tu; Hieu, Tran Dinh; Nguyen, Tan N.; Le, Thanh-Lanh; Nguyen, Sang Quang; Vozňák, Miroslav
Within the realm of sixth-generation (6G) wireless systems, there exist two primary imperatives: establishing massive connections and ensuring robust data transmission security. Therefore, this paper delves into the realm of physical layer security (PLS) within the context of a reconfigurable intelligent surface (RIS)-assisted Non-Orthogonal Multiple Access (NOMA) network coupled with the Internet of Things (IoTs), while addressing the challenge of non-concluding eavesdroppers. Specifically, the utilization of NOMA technology is anticipated to yield a substantial enhancement in spectrum efficiency for 6G and forthcoming wireless networks. Furthermore, this study investigates the security aspects through metrics such as the secrecy outage probability (SOP) and the average secrecy capacity (ASC), with the derivation of closed-form approximations for these metrics. Based on these mathematical expressions, we unveil the asymptotic Secrecy Outage Probability (SOP) to extract comprehensive insights into the RIS-assisted NOMA system’s behavior. Furthermore, we employ an algorithm based on the Golden Section to showcase the optimal SOP for a more in-depth analysis. Our findings highlight that the number of RIS metasurface components and the average signal-to-noise ratio at the access point are the primary factors driving improvements in system performance. Finally, we confirmed the correctness of our derived expressions by conducting a comparative analysis between Monte-Carlo simulations and analytical results.
A metaverse framework for IoT-based remote patient monitoring and virtual consultations using AES-256 encryption
(Elsevier, 2024) Mohammed, Zainab Khalid; Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; Zebari, Dilovan Asaad; Lakhan, Abdullah; Marhoon, Haydar Abdulameer; Nedoma, Jan; Martinek, Radek
The convergence of Internet of Things (IoT) and metaverse technologies is revolutionizing healthcare. This study introduces a pioneering framework tailored for health monitoring within the metaverse. By reshaping remote patient monitoring and virtual consultations, the framework utilizes vital parameters like heart rate, blood pressure, and body temperature. It integrates IoT sensors, augmented reality (AR), and virtual reality (VR), establishing a cohesive metaverse environment for healthcare interactions. Notably, robust 256-bit AES encryption ensures data privacy and security. Our analysis highlights the pivotal role of metaverse architecture in healthcare, emphasizing the efficacy of AES-256 encryption in preserving patient confidentiality. Findings underscore the framework's potential to enhance remote patient care while upholding stringent data privacy standards. Moreover, it fosters trust among patients, healthcare providers, and regulatory bodies. In summary, this comprehensive framework marks a significant advancement in remote patient care, promising improved health outcomes and a secure foundation for healthcare in the metaverse.
OOA-modified Bi-LSTM network: An effective intrusion detection framework for IoT systems
(Elsevier, 2024) Chintapalli, Siva Surya Narayana; Singh, Satya Prakash; Frnda, Jaroslav; Divakarachari, Parameshachari Bidare; Sarraju, Vijaya Lakshmi; Falkowski-Gilski, Przemysław
Currently, the Internet of Things (IoT) generates a huge amount of traffic data in communication and information technology. The diversification and integration of IoT applications and terminals make IoT vulnerable to intrusion attacks. Therefore, it is necessary to develop an efficient Intrusion Detection System (IDS) that guarantees the reliability, integrity, and security of IoT systems. The detection of intrusion is considered a challenging task because of inappropriate features existing in the input data and the slow training process. In order to address these issues, an effective meta heuristic based feature selection and deep learning techniques are developed for enhancing the IDS. The Osprey Optimization Algorithm (OOA) based feature selection is proposed for selecting the highly informative features from the input which leads to an effective differentiation among the normal and attack traffic of network. Moreover, the traditional sigmoid and tangent activation functions are replaced with the Exponential Linear Unit (ELU) activation function to propose the modified Bi-directional Long Short Term Memory (Bi-LSTM). The modified Bi-LSTM is used for classifying the types of intrusion attacks. The ELU activation function makes gradients extremely large during back-propagation and leads to faster learning. This research is analysed in three different datasets such as N-BaIoT, Canadian Institute for Cybersecurity Intrusion Detection Dataset 2017 (CICIDS-2017), and ToN-IoT datasets. The empirical investigation states that the proposed framework obtains impressive detection accuracy of 99.98 %, 99.97 % and 99.88 % on the N-BaIoT, CICIDS-2017, and ToN-IoT datasets, respectively. Compared to peer frameworks, this framework obtains high detection accuracy with better interpretability and reduced processing time.
Developing an IoT-enabled probabilistic model for quick identification of hidden radioactive materials in maritime port operations to strengthen global supply chain security
(Sage, 2024) Jakovlev, Sergej; Eglynas, Tomas; Jusis, Mindaugas; Vozňák, Miroslav
Uncovering hidden radioactive materials continues to be a major hurdle in worldwide supply chains. Recent research has not adequately investigated practical Internet of Things (IoT)-based approaches for improving and implementing efficient data fusion techniques. Current systems often misuse resources, leading to security vulnerabilities in typical settings. Our research delves into the fundamental principles of detection using both single and multiple sensor configurations, adopting a probabilistic method for merging data. We introduce a model aimed at accelerating the detection of radiation emissions in actual port operations. The results highlight the model’s effectiveness in rapid identification and determine the best conditions for its application in scenarios involving stacked containers, whether they are on ships or positioned in storage areas.

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