5G aims to revolutionize the way we connect and communicate by delivering higher data rates, ultra-low latency, massive device connectivity, and enhanced reliability. It works with millimeter-wave and sub-6 gigahertz frequencies, among many other frequency bands, enabling increased network capacity and improved user experiences. In this paper, a scenario is designed for a dense area with a large number of users. A FEMTO cell is also created for specific users with connectivity issues because they are located at the cell boundary area. Then, three existing scheduling algorithms namely Proportional Fair (PF), Modified Largest Weighted Delay First (MLWDF), and Exponential Proportional Fairness (EX-PF) are employed for the designed scenario. Afterward, algorithms are tested for different types of data flows such as Video and VoIP. Then, the simulation results are compared with each other and analyzed on various metrics such as system throughput, packet loss ratio (PLR), and latency. Finally, it is determined that in terms of throughput, latency, and PLR, EX-PF and MLWDF were doing well.

Predicting blood glucose is highly significant for patients with diabetes to manage their condition efficiently. Deep learning (DL) approaches have demonstrated great potential in blood glucose prediction modeling. By leveraging time-series data from continuous glucose monitoring (CGM) devices, this model can capture complicated temporal dependencies and patterns in glucose dynamics. DL based blood glucose prediction models learn from insulin dosages, past glucose readings, physical activity levels, meal intake, and other related features to forecast future blood glucose levels with maximum accuracy. Thus, this study presents an Optimal Attention-based Long Short-Term Memory for Blood Glucose Level Prediction (OALSTM BGLP) model. Firstly, it employs Min-Max scaling to normalize the input data, ensuring consistent and meaningful comparisons across different features. Additionally, the model generates time series data for multiple forecasting horizons, including 15, 30, 45, Aand 60-minute(m) intervals, enabling flexible and dynamic predictions to accommodate various planning and decision-making needs. Moreover, the OALSTMBGLP technique uses the ALSTM model, which incorporates an attention mechanism to selectively concentrate on relevant information within the input sequence while capturing long-term dependencies. This attention mechanism permits the method to effectively extract salient features from the input data, enhancing its predictive capabilities. Furthermore, the model is optimized using the RMSProp optimizer, which adjusts the rate of learning dependent on the magnitude of recent gradients, facilitating efficient training and convergence. The performance evaluation of the developed technique on the OhioT1DM dataset shows its promising performance over recent state-of-the-art methods.

The Internet of Things (IoT) and intelligent systems have emerged as two significant technological advancements in recent years. These ideas are now fundamental to many industries, including manufacturing, healthcare, transportation, agriculture, and even urban planning. Although these two terms are closely related, they have different fundamental perspectives, characteristics, and uses. This study aims to discuss the characteristics, similarities, and relationships of intelligent systems and IoT, as well as their challenges, future directions, and opportunities to enhance the automation, decision making, and productivity of real-life problems. Intelligent systems are developed to make decisions for the sake of humans using complex techniques like logic-based systems, learning and artificial intelligence while IoT is an extension of the cyber physical world where objects can interact with each other and share data. Together, these technologies enable automated systems, improve the operations, and support real time decision making. Furthermore, the study also discusses the potential issues in the development and application of intelligent systems and IoT, such as security, scalability, and interoperability concerns. It also outlines some future directions, including the environments growing that tendency can of respond combining to AI changes based in intelligent the systems system. with Other IoT aspects to of generate these intelligent technologies and including adaptive their components and their applications are also thoroughly discussed to demonstrate how they enable innovations such as smart cities, precision agriculture, predictive healthcare, and advanced manufacturing systems. In this paper, the combined use of the strength of IoT and intelligent systems is proposed to lead to the development of highly automated systems and intelligent environments to enhance the way of life in the future.

Action recognition in videos is one of the essential, challenging and active area of research in the field of computer vision that adopted in various applications including automated surveillance systems, security systems and human computer interaction. In this paper, we present an in-depth comparative analysis of five CNN-RNN models based on pre-trained networks such as InceptionV3, VGG16, MobileNetV2, ResNet152V2 and InceptionResNetV2 with recurrent LSTM units for action recognition on Anomaly-5 dataset. The performance of these models is analyzed and compared in terms of accuracy, precision, recall & F1-scores and computational efficiency. The CNN-RNN architectures we considered for analysis in this paper, the ResNet152V2 based CNN-RNN model exhibits better performance and achieved highest accuracy, precision, recall and F1-score equal to 92.20% due to its ability to capture more complex spatial features. This comparative analysis may guide the researchers in selecting appropriate models for real-world applications for action recognition. In addition of this, a new dataset is developed called Anomaly-5 that can helps as a valuable resource for training and evaluating action recognition algorithms.

Every country’s income depends heavily on the agriculture sector. Agriculture output is influenced by a wide range of factors, including crop health, weather, water availability, and type of land. Low output is primarily caused by crop disease. The Identification and treatment of diseases early on are very crucial. Traditional approaches don't offer a reliable evaluation. The spread of the disease must be determined using automated techniques. One of the cutting-edge technologies that aids in automating disease detection is deep learning. Deep learning has been used in numerous studies to detect crop diseases. The goal of this research is to effectively diagnose leaf diseases in all major crops using a novel deep learning model using a modified ResNet50. The research employed different deep learning models like VGG16, Inception V3 and modified ResNet50 on publicly available dataset of plant village. A novel approach has been proposed by extracting features from different models. The ResNet50 uses the skip connection which is considered to training and test error. The modified ResNet50 model helps add diversity in different crops for providing accuracy across all crops on public dataset of plant village. The dataset was obtained from a publicly available database on Kaggle. The dataset contains 13 different crops and a total of 38 diseases classes. On modified plant villages the proposed model generated a very good accuracy of 99.49% with precision of 0.966 and f score of 0.995. The proposed model is reliable for the classification of various leaf diseases in all the major crops.

Controller Pilot Data Link Communications (CPDLC) enhances air traffic communication by replacing traditional voice transmissions with digital messages over Very High Frequency (VHF) radio systems. This transition improves communication resilience by providing clear, text-based instructions that reduce misunderstandings and increase bandwidth efficiency by enabling more data to be transmitted simultaneously. It benefits congested airspace by reducing radio frequency congestion and minimizing communication errors. However, due to the plain-text nature of its messages, CPDLC faces significant security challenges, making it vulnerable to cyber-attacks such as eavesdropping, modification, injection, and man-in-the-middle (MITM) attacks. This vulnerability allows motivated attackers to intercept CPDLC messages using inexpensive devices like Software-Defined Radio (SDR), HACKRF-one, and an antenna. Such breaches can lead to fatal safety incidents, severely impacting passengers and the aviation industry. To address this, we proposed a robust security framework for securing CPDLC communication by implementing critical measures, including mutual authentication, secure key establishment, and handover. The proposed framework has been tested on hardware to verify its effectiveness in practical scenarios, ensuring it aligns with existing CPDLC standards and integrates seamlessly into current systems without impacting operational efficiency. Our findings indicate that the proposed security framework enhances CPDLC's defenses against potential cyber threats while maintaining system performance, making it feasible to protect global air traffic communications.

Gradually, the 6th Generation cellular network standard is being adopted (6G). Due to the creative and distinctive features of 6G, such as the capacity to deliver connection even in space and underwater, governments, corporations, and academics are investing a substantial amount of time, money, and effort in this subject. The next generation technology will be effective around the year 2030–2035. The next generation will be able to provide strong connectivity between machines and mobile devices. The speed can reach 1 Gbps in the downlink and 500 Mbps in the uplink. Edge computing, blockchain, advanced IoT and Li-Fi (Light Fidelity) technologies, as well as Artificial Intelligence (AI), are believed to provide the foundation for forthcoming innovations. Further research into the fundamental architecture of 6G is necessary to pinpoint these areas for development. We have examined a variety of concerns that might arise while employing next generation communication technologies, such as security and practical concerns. All of the physical layer's facts, which are crucial for implementation, were taken into account during the study. The conclusion of the study includes all the pertinent information and arguments that researchers might take into account while they work on next generation communication technologies.

Agriculture is widely affected by various diseases. The traditional methods of detecting diseases are time-consuming and labor-intensive. Deep learning is one of the most emerging fields. It has largely evolved in recent years because of its automatic feature extraction mechanism. Deep learning in agriculture has shown promising results for analyzing various agricultural aspects. Because of the automated methods deep learning has shown very promising results in finding leaf diseases automatically without requiring human intervention. There are many crops which play a big role in the economy of any county. One of the vegetables that is most frequently used in cooking is the tomato. The tomato crop is more vulnerable to diseases. The diseases in the tomato crop cause significant damage to the crop. It is difficult to identify the disease early because of many constraints. Agronomists and farmers can more efficiently and promptly manage the crop with the use of a deep learning-based method for detecting tomato leaf diseases. In this research, we have trained various deep learning models on the publically available dataset of tomatoes which contained 9 leaf diseases and 1 healthy. The models used are VGG16, MobileNetV2, SqueezNet and MobileNetV3Small. Different models provided a different accuracy on the public dataset. MobileNetV3Small model showed a very promising accuracy of 99.43% on 22930 leaf images of tomatoes. The accuracy of the model achieved on 25 epochs.

IEEE 802.15.4 defines the working of physical and media access layers of a Low-Rate Wireless Personal Area Network (LR-WPAN). A LR-WPAN is a low cost, low power, and low data-rate network that offers reasonable lifetime and reliable data transfer within a limited range. However, it faces several challenges whilst dealing with applications that are having strict timeliness, energy, and bandwidth requirements. This paper proposes an efficient superframe structure for the MAC layer of IEEE 802.15.4 networks that intends to deal with these challenges by varying the functionalities of Guaranteed Time Slot (GTS) bits. Simulations of different GTS allocation techniques show that our enhanced scheme outperforms the original standard as well as previous techniques in terms of energy consumption, average delay, maximum GTS allocation and reliability.

Sixth-generation mobile networks (6G) are expected to reach extreme communication capabilities to realize emerging applications demanded by the future society. This paper focuses on six technological directions towards 6G, namely, intent-based networking, THz communication, artificial intelligence, distributed ledger technology/blockchain, smart devices and gadget-free communication, and quantum communication. These technologies will enable 6G to be more capable of catering to the demands of future network services and applications. Each of these technologies is discussed highlighting recent developments, applicability in 6G, and deployment challenges. It is envisaged that this work will facilitate 6G related research and developments, especially along the six technological directions discussed in the paper.

The thermal-convective instability of a stellar atmosphere in the presence of a stable solute gradient in Stern’s type configuration is studied in the presence of suspended particles. The criteria for monotonic instability are derived which are found to hold well in the presence of uniform rotation and uniform magnetic field, separately, on the thermosolutal-convective instability of a stellar atmosphere in the presence of suspended particles.

Controller-Pilot Data Link Communications (CPDLC) are rapidly replacing voice-based Air Traffic Control (ATC) communications worldwide. Being digital, CPDLC is highly resilient and bandwidth efficient, which makes it the best choice for traffic-congested airports. Although CPDLC initially seems to be a perfect solution for modern-day ATC operations, it suffers from serious security issues. For instance, eavesdropping, spoofing, man-in-the-middle, message replay, impersonation attacks, etc. Cyber attacks on the aviation communication network could be hazardous, leading to fatal aircraft incidents and causing damage to individuals, service providers, and the aviation industry. Therefore, we propose a new security model called AKAASH, enabling several paramount security services, such as efficient and robust mutual authentication, key establishment, and a secure handover approach for the CPDLC-enabled aviation communication network. We implement the approach on hardware to examine the practicality of the proposed approach and verify its computational and communication efficiency and efficacy. We investigate the robustness of AKAASH through formal (proverif) and informal security analysis. The analysis reveals that the AKAASH adheres to the CPDLC standards and can easily integrate into the CPDLC framework.

Physical layer security (PLS) has grown in popularity as it guarantees secure and reliable communication between wireless devices without restricting the ability of data analysts to eavesdrop. PLS benefits from the randomization property of the wireless channel, which improves immunity and protects against many dangers of information breaches from eavesdroppers. MIMO and Massive MIMO, which use numerous antennas at the transmitter and receiver ends, are fundamental technologies for improving efficiency in 5G and future network systems. The PLS still requires a significant amount of work before it can be employed in practical systems. The interception of eavesdroppers can be made difficult by adding artificial noise. This paper focuses on the physical layer security considering maximal ratio combining, and artificial noise with beamforming techniques to further enhance the secrecy rate. The proposed work demonstrates that the use of transmitter beamforming with artificial noise can significantly improve the degree of confidentiality of information being transmitted between a transmitter and an intended receiver while making it more difficult for eavesdroppers to intercept. The simulation results in terms of bit error rate show that the proposed method has a significant impact on the error rate performance of the eavesdropper.

Technological advancements and miniaturization of sensor have made it possible to use tiny size sensors nodes to monitor patients’ physiological data in real-time at very low cost. The network of such small size sensor nodes is often called wireless body sensor networks (WBSNs). The patient’s data is highly sensitive which required to reach reliably and timely at the destination node. However, short range wireless nodes and postural body movements cause frequent topological changes and recurrent network partitioning. The network partitioning leads to delayed and significant loss of critical patient’s data such as Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyogram (EMG). Therefore, QoS requirement of WBSNs in terms of reliability, end-to-end delay and throughput are highly compromise. We in this paper propose a Predicting routing protocol PLQE to address reliable data transmission and network partitioning due to node mobility. The PLQE dynamically determines the efficient links by using beta probability density function, link quality and link delay estimation (LDE). The PLQE is composed of link reliability factor (LRF) and expected probability Indicator (EPI) to obtain most often updated status of the links to figure out stable and reliable end to end route to the destination node. The simulation results confirm the higher performance of the proposed scheme against state-of-the-art routing protocols in terms of packet delivery ratio, end to end delay, throughput and normalized routing load.

Mobile wireless sensor networks (MWSNs) are a new development of static wireless sensor networks that allow the mobility of sensor nodes. Mobile sensor nodes are fundamental components of MWSNs. Because of this mobility of sensor nodes, the link between sensor nodes frequently changes, and packet loss occurs. Despite the packet loss of MWSNs, they also face challenges in energy consumption, communication overhead, and others. This research aims to analyze the performance of the hierarchical routing graph construction (HRGC) routing scheme. Its working mechanism and network structure suit all real-time applications with involved mobility. We discuss the limitations of the HRGC scheme theoretically and propose an improved routing scheme for data transmission in MWSNs based on HRGC. It should overcome the main limitations of HRGC, such as high data communication overhead, high energy consumption during network maintenance, data transmission process, and route failure issues due to mobility. Keywords: mobile wireless sensor networks, routing protocols, data transmission, sink, sensors. https://doi.org/10.55463/issn.1674-2974.50.9.6

Early detection of diabetes plays a crucial role in improving health outcomes and can help people avoid harmful diabetes complications. Machine learning algorithms are being used to diagnose a disease in its early stages. This study proposes an optimized weighted ensemble model that can predict the risk of type 2 diabetes mellitus. A diabetes dataset of 403 patients from the Department of Medicine at the University of Virginia given by Dr. John Schorling has been used. We assessed ridge regressor, LASSO, feedforward artificial neural networks, and linear regression prediction performance. These models were then combined to create an optimized weighted ensemble model for prediction. We evaluated our prediction models using standard performance metrics: coefficient of determination (R2 score), root mean square error (RMSE), mean square error (MSE), and mean absolute error (MAE). The results showed that the proposed optimized weighted ensemble model outperformed individual models, achieving the highest 0.81 (R2 score) and lowest 0.98 (MSE).

Mobile Ad hoc Networks (MANET) is a collection of mobiles and smart devices that communicates using wireless links. These types of networks are growing at the lightning speed and are being used at a large scale than ever before because of their high availability and mobility. As the usage of MANET devices increase, the challenges also increase for example the proper delivery of data or information to every mobile node. Traditionally, transmission control protocol (TCP) is used for delivery but it does not fit in all scenarios due to some of its limitations such as network congestion and quick recovery in case of link failure. In this, a comparative analysis of TCP is performed with the stream control transmission protocol (SCTP) and a TCP variant called TCP VEGAS. Several ad hoc scenarios are proposed with varying numbers of mobile nodes, i.e. 11, 21 and 31. The results obtained indicate that SCTP outperforms TCP and VEGAS in throughput. While using SCTP the packet lifetime is also small i.e. 0.19 seconds. The link utilization of SCTP is better than TCP and VEGAS. However, TCP increases its link utilization more than VEGAS for the nodes in MANET of large delay.

The present trend of automation anddata interchange in industrial technology is known as Industry 4.0. Industry 4.0 is altering its next generation of distribution networks by making them more responsive and efficient. There are various issues when implementing Internet of Thing (IoT) devices in an Industry 4.0 scenario, mainly due to reduced IoT nodes or devices with insufficient infrastructure to operate security solutions. As a result, securing the environment will require a lightweight and effective security solution. The authors of this study offer a lightweight and flexible authentication method as part of an access control strategy for secure data exchange in-vehicle networks. Lightweight operations, hash functions, XOR operations, and concatenation are all used in the proposed protocol. The security of this approach is also evaluated using AVISPA. The authentication process between IoT networks and resilience to various security threats will be demonstrated. The experimental analysis shows that the computational cost attained is 7.96 s, and the communication cost attained is 834 bits. Similarly, the resource utilization for this computation is 11%, and the average verification time is 7 ms. The experimental analysis demonstrates that the proposed method is efficient with lower computational cost, communication costs, verification cost, and resource utilization than the existing state-of-the-art approaches