Connectivity Of Devices Research Articles

Cyber Guardian : Intelligent Threat Surveillance

Advanced persistent threats (APTs) are cyberattacking that use covert strategies to target specific groups. As a result of the rapid growth of computing technology and the widespread connectivity of devices, there has been a boom in data transfer across networks. Because APTs' attack tactics are always changing, it can be difficult to detect them. This has led cybersecurity experts to develop creative solutions. We found gaps in the research on APT detection by doing a systematic literature review (SLR) covering the years 2012 to 2022 and finding 75 studies related to computer, mobile, and Internet of Things technologies. The most sophisticated cyberattack, known as an advanced persistent threat, involves malevolent individuals breaking into a network without authorization and staying hidden for an extended period. Advancement persistent threat attacks and organizational threats are becoming more frequent. Machine learning is one technique used to detect attacks by sophisticated persistent threats. The need for improved detection methods is highlighted by our findings, and we offer suggestions to guide the creation of early APT detection models and progress in cybersecurity. We propose a conceptual model known as Cyber Guardian that uses Random Forest classifier and attention techniques to create a self-translation machine through an encoder-decoder framework. These advanced attention algorithms are intended to improve the machine's capacity to examine and decipher intricate patterns found in HTTP requests, enhancing APT detection capabilities, and providing cybersecurity experts with cutting-edge instruments to proactively detect and neutralize new threats in real-time. This all-encompassing strategy is a major advancement in the ongoing fight against Advanced Persistent Threats (APTs) and emphasizes how crucial it is for the cybersecurity community to continuously innovate and collaborate in order to remain ahead of changing cyberthreats.

Control of three‐level bidirectional buck‐boost converter for battery energy storage system in bi‐polar DC microgrid

AbstractThis paper deals with the model predictive current control of a three‐level bidirectional buck‐boost converter for a battery energy storage system in a bi‐polar direct current (DC) microgrid. It comprises a solar photovoltaic system, a wind energy system, a battery and DC loads. The proposed control scheme provides voltage balance and voltage regulation at the bi‐polar DC link during dynamic operating conditions, such as variations in solar irradiance, wind velocity and load. Predictive current control utilises the converter's discrete behaviour to identify the suitable switching states that minimise the cost function. Furthermore, bidirectional power flow capabilities allow the connection of energy storage devices, such as batteries and ultra‐capacitors, to the bi‐polar DC microgrid. The proposed system is verified through simulations and validated on a lab‐scale prototype. The results have been found satisfactory with respect to steady‐state error, peak overshoot, and settling time.

FOG-RPL: Fog Computing-based Routing Protocol for IoT Networks

Background: The Internet of Things (IoT) is widely used because of the connectivity of devices with the Internet which provides accessibility, quick transmission, and broader coverage. IoT networks provide vast connectivity but finding the best path for sharing information is a big challenge because of limited resources like limited power and limited bandwidth. The routing protocol for low power lossy network (RPL) is standard protocol but it selects a node that has already been selected in a busty network. Methods: The fog computing technique is combined with RPL and the new objective function is used to design FOG-RPL which is the optimum routing protocol that reduces the network load using the fog computing principle and selects the right node using the new objective function. Results: The simulation is performed and experimental results show that FOG-RPL gives better results in terms of improvement and in terms of performance parameters. Conclusion: The FOG-RPL protocol uses the fog computing principle with a new objective function and performance analysis shows that as compared to the existing routing protocol, it is more efficient.

Examining of preparatory and first-year students’ online learning readiness and presence in English language courses

This study aims to investigate university students' readiness and presence towards online teaching in the context of various variables. The study is designed as survey research, one of the quantitative models. The sample of the study consists of 318 preparatory and first-year university students studying at a university in the Eastern Anatolian Region of Turkey. The data were obtained using the "Readiness for Online Learning Scale" and "The Community of Inquiry Model " scale. Descriptive statistics and inferential were used in the analysis of the data. As a result of the study, it was revealed that university students' readiness levels for online learning and their social, cognitive and teaching presence were high. In addition, it was determined that university students' readiness levels and perceptions of presence differ statistically according to age, gender, education level, monthly income of the family and connection device. The implications were discussed in terms of theoretical insights and administration for online learning.

An Essay on Annotating Connectives in a Japanese Text – Lessons and Prospects

This paper offers an overview of an attempt to annotate a text in Japanese with the aim of pointing out all possible connections between elementary chunks of discourse, and the connective devices marking them. Being the first experience in Japanese, it asks for a certain adjustment of the annotation rules already used in the existing schools. This primarily touches upon what entities are connected and also upon the definition of a connective. Furthermore, semantic areas covered by connectives also need correction if reviewed through an in-depth analysis of the speaker’s intentions. The obstacles met while carrying out the annotation focus around two linguistic factors, as do the pervasive patterns revealed. On the one hand, the structural specifics of the Japanese language cause the speaker to forego multiple transformations that warp the surface structure in order to attain linear development of his or her line of discourse. On the other hand, the genre of the linguistic material (a lecture in tourism marketing) is largely accountable for dominating Causality and Discourse Deployment areas of connectives. It is through those that a higher level of pragmatic motivation is achieved in the explicit markers of the text structure – which turns out crucial in making the speech more persuasive.

Impact of 5G Wireless Technologies on Cloud Computing and Internet of Things (IOT)

With its promise of unmatched speed, low latency, and widespread device connection, the introduction of 5G wireless technology represents a critical turning point in the development of telecommunications. This study examines how 5G would significantly affect cloud computing and the Internet of Things (IoT), two important technical areas.5G brings about a paradigm change in the field of cloud computing by facilitating quicker and more dependable connectivity between end-user devices and cloud servers. High data transmission rates and low latency enable real-time service delivery and processing, creating new opportunities for resource-intensive applications like edge computing, virtual reality, and augmented reality. The integration of 5G with cloud computing has the potential to transform cloud-based services' architecture and functionalities, enhancing their responsiveness and dynamism. Moreover, a new age of automation and communication is heralded by the merging of 5G with IoT. The large flood of IoT devices is accommodated by 5G's expanded network capacity, which facilitates smooth communication and coordination amongst them. By enabling IoT applications with never-before-seen speed and dependability, this synergy opens up new opportunities in industries including industrial automation, smart cities, healthcare, and agriculture. The convergence of 5G and IoT not only expedites the implementation of IoT solutions but also amplifies their efficiency and scalability. But there are obstacles in the way of 5G's revolutionary promise. As more and more devices are connected and data is exchanged, security and privacy issues become critical. In addition, significant financial outlays and careful planning are needed to meet the infrastructure needs for the rollout of 5G. The symbiotic link between 5G, cloud computing, and the Internet of Things is thoroughly examined in this article. It seeks to contribute to the knowledge of the technical environment and direct future research and development activities in order to fully realize the promise of 5G-enabled innovations by analysing the possibilities and difficulties posed by this convergence.

ZnO nanoparticles and carboxylated MWNTs multi-networks composite hydrogels with piezoelectric properties and human motion sensing

Piezoelectric composite hydrogels have been developed as interfaces connecting biological organs and electronic devices owing to their flexibility, biocompatibility, and electromechanical behavior, which enable the conversion of environmental stimuli into electronic signals. This study presents a multifunctional hydrogel that is soft, deformable, conductive, and piezoelectric. Using water as solvent and ZnO carbon nanotube (CNT) modified polyvinyl alcohol/agar/tannin as active ingredient, a one-pot green solution mixing method was used to prepare piezoelectric hydrogel with multi-network structure. The T5C5Z4 composite hydrogel designed in this paper has excellent performance in the sensing test, and can also clearly and accurately generate signals for slight movements of body parts. The T5C5Z4 composite hydrogel also has stable piezoelectric performance. In this paper designs a 100 g ball with a radius of 10 mm that falls freely from different heights and contacts with composite water (6 cm*3 cm*4 mm) to output different electrical signals, and the output voltage also increases with the height with increasing. The composite hydrogel has also been demonstrated as a sensing-detectable, piezoelectrically stimulated flexible bioelectronic materials.

AN ANALYSIS OF THE HETEROGENEOUS IOT DEVICE NETWORK INTERACTION IN A CYBER-PHYSICAL SYSTEM

The article is devoted to the study of existing technologies regarding Internet of Things (IoT) device interaction in a heterogeneous network. Since each smart home appliance can be controlled by a customer who aims to find a cost-effective and easy-to-connect product for their own connected home, there are certain functional limitations for devices from distinct manufacturers that may decrease the intention to merge them all into a single network. A variety of proprietary protocols and communication standards embedded by vendors make their products unable to interact with other vendor devices if the connection standard used is not identical. Also, an IoT product design refers to its own functionality, mainly excluding the possibility of integration into other existing infrastructure. As IoT equipment emerges on the market, the complexity of its connection to a heterogeneous network corresponds to the firmware and the standard unification according to modern demands. It means that potential users might face the necessity of overcoming these issues to achieve high performance in terms of network interoperability. In general, an IoT gateway operating as a middleware might have the potential to enable a network with distinct communication models to operate without failure or data loss. This task requires the received data to be converted into the format in which the data is intended. This paper includes a comparative analysis of existing IoT device interaction standards, connection protocols, and data transfer technologies, evaluating their features for an effective adoption of the proposed network architecture, which can be used to improve the interoperability of heterogeneous IoT devices.

Exploiting Satellite Doppler for Reliable and Faster Data Download in IoT Satellite Networks

Low Earth Orbit (LEO) satellite constellations are enhancing IoT device connectivity with cost-effective, easily deployable picosatellites. These IoT satellite constellations bypass the need for Earth-based gateways, offering global coverage. This article delves into communication challenges encountered when deploying an IoT satellite network. Shifting the classic perspective on Doppler from a hindrance to an advantage, we present novel techniques leveraging Doppler shift for reliable packet detection and decoding, particularly in noisy environments. Integrated into our system, Spectrumize [1], these techniques offer a substantial performance boost compared to conventional methods.

Profiling Running Applications in Connected Devices Through Side-Channel and Machine Learning Techniques

Profiling Running Applications in Connected Devices Through Side-Channel and Machine Learning Techniques

A Novel Experience-Driven and Federated Intelligent Threat-Defense Framework in IoMT.

The Artificial Intelligence-enabled Internet of Medical Things (AI-IoMT) envisions the connectivity of medical devices encompassing advanced computing technologies to empower large-scale intelligent healthcare networks. The AI-IoMT continuously monitors patients' health and vital computations via IoMT sensors with enhanced resource utilization for providing progressive medical care services. However, the security concerns of these autonomous systems against potential threats are still underdeveloped. Since these IoMT sensor networks carry a bulk of sensitive data, they are susceptible to unobservable False Data Injection Attacks (FDIA), thus jeopardizing patients' health. This paper presents a novel threat-defense analysis framework that establishes an experience-driven approach based on a deep deterministic policy gradient to inject false measurements into IoMT sensors, computing vitals, causing patients' health instability. Subsequently, a privacy-preserved and optimized federated intelligent FDIA detector is deployed to detect malicious activity. The proposed method is parallelizable and computationally efficient to work collaboratively in a dynamic domain. Compared to existing techniques, the proposed threat-defense framework is able to thoroughly analyze severe systems' security holes and combats the risk with lower computing cost and high detection accuracy along with preserving the patients' data privacy.

Investigation of the Real Efficiency of a Decentralized Heat Recovery Ventilation System

The paper presents the measurement method and values of the temperatures of the incoming and outgoing air obtained as a result of the connection and automation of 4 decentralized heat recovery ventilation units into one system, and the measured results of the thermal efficiency of the entire system. The built system consists of four single-room heat recovery devices, which are integrated into an automated system. The built-in sensors can be used to measure the average temperature of the ventilated rooms or the current temperature and relative humidity of the air flowing through the ventilation units, as well as the carbon dioxide level of the rooms. The units received automation that complements the original individual control elements, which is capable of complex control tasks from any computing device, graphic interface and Wi-Fi connection. The units' sensors, which can be moved into the air stream, are suitable for the momentary measurement of the parameters of the flowing air, thereby estimating the thermal efficiency of the entire system.

Change in paradigm of medical undergraduate teaching due to COVID-19 pandemic students’ perspective: A cross-sectional survey

Abstract: Introduction: The use of competency-based, integrated teaching and self-directed learning has brought about substantial changes in the field of medical education in recent years. However, the COVID-19 epidemic suddenly changed the game, requiring an immediate switch to online instruction. Aim: This study examines undergraduate medical students’ experiences in a private medical institution during the COVID-19 epidemic by contrasting traditional in-person instruction with recently implemented online learning techniques. Materials and Methods: A cross-sectional web-based survey was conducted among students belonging to medical fraternity. Response rate of 97.2% of the students took part. We looked at learning outcomes, motivation, interaction, and attentiveness as well as demographics, device and Internet connection choices, and the effects of online and offline learning environments. Results: We found that students significantly prefer traditional in-person instruction, with lecture halls being preferred over online learning environments. Issues with motivation, focus, and the capacity to learn efficiently in an online setting are among the difficulties noted. According to the report, some students support hybrid learning, even if the majority still prefers offline instruction. The results highlight the need for a sophisticated strategy that integrates the best aspects of online and traditional teaching methods into a “blended learning” model. Conclusion: This study emphasizes the significance of responding student input to improve the quality of medical education, which provides insightful information for improving instructional practices in the post-COVID-19 age.

Development of an combined energy complex consisting of a micro-hydroelectric power plant and a solar photoelectric plant for the conditions of Uzbekistan

The article develops a block diagram of an energy device that serves as the embodiment of a microhydroelectric power station and a solar photovoltaic installation. Based on this structural diagram, a combined energy device consisting of a solar photovoltaic power plant and a microhydroelectric power station was developed and installed for experimental research at the family enterprise “Akbarzhon Korki” in the Sokchilik microdistrict, Toshlok district. Based on the needs of the family business, the electricity generated by a combined power plant consisting of a 4 kW solar photovoltaic system and a 0.7 kW microhydroelectric power plant was used for lighting systems, a refrigerator, an electric cooking stove, and a modem. Internet connection device and video surveillance equipment. As a result of the implementation at the family enterprise, 2 124 000 soums of electricity produced by the combined energy device were used for the enterprise’s own needs during the year, and economic efficiency of 12 599 000 soums was achieved.

Метод оптимизации размещения компенсирующих и симметрирующих устройств в электрических сетях низкого напряжения

The paper studies the optimization features of low-voltage electrical network modes for energy-efficient power transmission. The main features of these networks are the high values of the zero-sequence voltage unbalance factor and the mismatch of voltage values with the necessary requirements. Therefore, balancing the loads by phases and reactive power compensation are chosen as measures to improve the listed network parameters. One of the main factors for the effective solution of this problem is the determination of load balancing points and reactive power compensation, as well as the required power of the devices used. To solve the multicriteria problem of determining device connection points, an analysis of existing objective functions is carried out. As a result, a target function is proposed that includes costs, voltage losses, zero-sequence voltage asymmetry coefficient. When solving the optimization problem with the help of the proposed objective function, the voltage values and the voltage asymmetry factor at the consumer connection points will be within the required limits. The results of a study of the optimization function using the example of a model network are presented. The calculations are carried out in two stages: the search for sensor's nodes, then their study for optimality. All calculations are carried out in the MATLAB environment, taking into account voltage losses and power.

A production scheduling and control system for CAR T cell manufacturing

This paper addresses some of the new challenges posed by scheduling and controlling the production of chimeric antigen receptor (CAR) T cells for personalized therapies. In this therapy, the patient’s T cells are genetically modified by adding a gene for a receptor to help T cells attach to the cancer cell antigens. Then, the modified cells are grown and multiplied (called cell expansion) and finally returned to the patient. The production takes place in a manufacturing system that comprises several devices, which are capable of processing cells from multiple patients at the same time. The efficient operation of such a complex system requires a sophisticated control system accompanied by a scheduler to optimize the production of CAR T cells for several patients simultaneously. We propose a scheduling algorithm, which can handle the uncertain duration of cell expansion, and the maximum time lags between consecutive processes simultaneously, a combination hardly studied in the literature. We also describe the main features of an adaptive control system including service-oriented device connections as well as centralized control and monitoring. Finally, we describe how the overall system works, including a JSON based communication between scheduler and control system.

Energy Management Model for a Remote Microgrid Based on Demand-Side Energy Control

The internet of things is undergoing rapid expansion, transforming diverse industries by facilitating device connectivity and supporting advanced applications. In the domain of energy production, internet of things holds substantial promise for streamlining processes and enhancing efficiency. This research introduces a comprehensive monitoring and energy management model tailored for the University of Cuenca’s microgrid system, employing internet of things and ThingSpeak as pivotal technologies. The proposed approach capitalizes on intelligent environments and employs ThingSpeak as a robust platform for presenting and analyzing data. Through the integration of internet of things devices and sensors, the photovoltaic system’s parameters, including solar radiation and temperature, are monitored in real time. The collected data undergo analysis using sophisticated models and are presented visually through ThingSpeak, facilitating effective energy management and decision making. The developed monitoring system underwent rigorous testing in a laboratory microgrid setup, where the photovoltaic system is interconnected with other generation and storage systems, as well as the electrical grid. This seamless integration enhances visibility and control over the microgrid’s energy production. The results attest to the successful implementation of the monitoring system, highlighting its efficacy in improving the supervision, automation, and analysis of daily energy production. By leveraging internet of things technologies and ThingSpeak, stakeholders gain access to real-time data, enabling them to analyze performance trends and optimize energy resources. This research underscores the practical application of internet of things in enhancing the monitoring and management of energy systems with tangible benefits for stakeholders involved.

Securing Iot Data Transmission: A Comprehensive Approach Integrating Two-Fish Encryption With Wireless Smart Energy Systems And Iot Cloud Services

This work addresses the challenges posed by the interconnected nature of the Internet of Things (IoT). This paradigm, facilitating the seamless connectivity of diverse devices, gives rise to issues such as addressability, confidentiality, and data management. The proposed solution advocates for the integration of the robust Twofish encryption algorithm into wireless smart energy systems, creating a holistic approach that capitalizes on the capabilities of IoT cloud services. Twofish, recognized for its strong encryption capabilities, becomes an integral part of the IoT framework to ensure the security of communication and data confidentiality. Through the amalgamation of Twofish encryption with wireless smart energy systems and IoT cloud services, the approach provides a comprehensive solution for the secure transmission and management of data. This integration enhances system scalability, flexibility, and accessibility, thereby safeguarding the confidentiality of information exchanged over the internet. The paper establishes the superiority of Twofish over lightweight cryptography for low-power devices within IoT systems through a thorough analysis of critical factors. These factors encompass the robust security strength of Twofish, its effective encryption capabilities guaranteeing data confidentiality, the algorithm's maturity validated by extensive scrutiny from the cryptographic community, compatibility and interoperability with diverse platforms, resource efficiency in maintaining a balance between security and device capabilities, and the crucial long-term security assurance required for the extended operational lifetimes of IoT devices. The research findings affirm that the incorporation of Twofish into wireless smart energy systems and IoT cloud services offers a secure and efficient solution for safeguarding sensitive information within IoT environments. This consolidated approach contributes significantly to advancing secure communication and data management in the continually evolving landscape of the Internet of Things.

Bagging ensemble mean-shift Gaussian kernelized clustering based D2D connectivity enabled communication for 5G networks

The advent of the fifth-generation (5 G) network integrates a spectrum of powerful technologies. Among these, Device-to-Device (D2D) communication stands as a focal point in research due to its expansive array of applications. Within the realm of 5 G, D2D communication offers support for high data rates and ultra-reliable, low-latency communications. While machine learning-based approaches have been devised to facilitate D2D communication in 5 G, ensuring robust connectivity and optimal network throughput remains a persisting challenge. Therefore, the bagging ensemble mean-shift Gaussian kernelized clustering-based D2D Connectivity (BEMSGKC-DC) algorithm is designed to enhance device-to-device communication in 5 G Cellular Networks with better packet delivery ratio and throughput. The proposed BEMSGKC-DC algorithm is proposed to enable massive device connectivity while improving the overall network performance. This can be achieved by employing bootstrap aggregating clustering where it clusters the devices via forming a set of base clusters i.e., Mean-Shift Gaussian kernelized clustering algorithm. To further enhance the performance of the algorithm, this paper also computes the resources of the devices in the clustering process using the Gaussian kernel function. All the weak cluster outcomes are merged to acquire the final clustering output with lower error. Through the resultant clustering of the device, the connectivity between D2D is ensured and thereby improves the data communication in the 5 G network. The result demonstrates that the BEMSGKC-DC algorithm optimizes the connectivity with lower energy, latency, and higher data delivery ratio and packet loss rate than the conventional algorithms.

Double‐deep Q‐learning‐based handover management in mmWave heterogeneous networks with dual connectivity

AbstractMillimeter wave (mmWave) technology, with its abundant spectrum resources and ultra‐high bandwidth, plays a crucial role in meeting the ultra‐high throughput requirements of future 6G communication and supporting a large number of device connections. However, mmWave signals are highly susceptible to congestion, and the channel quality in mmWave links can be extremely unstable, posing a significant challenge to mobility management in mmWave networks. In this article, we present a deep reinforcement learning‐based dual connectivity (DC) handover strategy for mmWave cellular networks. The DC architecture ensures a continuous connection to the core network, reducing the likelihood of frequent service interruptions. Additionally, the proposed handover strategy employs deep reinforcement learning to select the optimal handover target node, further enhancing system performance. Compared to traditional single connectivity (SC) handover strategies, DC involves more intricate handover trigger conditions and processes. We extend several typical SC handover algorithms for DC and compare them with the proposed algorithms in this study. Through numerical simulations, we evaluate the performance of these algorithms. The results demonstrate that the proposed algorithm minimizes unnecessary handovers, reduces service interruption time, and achieves the highest throughput.