International Journal of Artificial Intelligence and Communication Networks
Enhanced Key Exchange And Lightweight Encryption For IoT Security Using Timestamp-Based OTP And Enhanced RSA
In the modern digital environment, ensuring secure data transmission between data owners and authorized users is essential for protecting sensitive information. This paper proposes a secure data-sharing framework that integrates One-Time Password (OTP) generation with an RSA-based key exchange mechanism. Whenever a data owner initiates a data-sharing process, a unique and dynamically generated OTP is created to serve as an access credential. The OTP is encrypted using the RSA algorithm before being transmitted, ensuring that only the intended and authorized user can decrypt and use it to access the shared data. The asymmetric encryption mechanism also helps establish a secure communication channel between the data owner and the recipient, reducing the risk of unauthorized access, interception, and misuse during transmission. By combining dynamically generated OTPs with the security capabilities of RSA encryption, the proposed framework strengthens authentication, confidentiality, and data integrity. This approach provides an effective solution for secure information sharing in digital environments where reliable access control and protection against security threats are essential.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0/
Attention-Driven Lightweight Network For Colorectal Cancer Classification
Colorectal cancer (CRC) is a major cause of cancer-related deaths worldwide, making early and accurate diagnosis essential for improving patient outcomes. Although histopathological analysis remains the standard method for CRC diagnosis, manual examination of tissue images can be time-consuming, labor-intensive, and susceptible to variations in human interpretation. To address these limitations, this study presents an efficient attention-based deep learning framework built on the MobileNetV2 architecture for the automated classification of colorectal cancer histopathology images. The proposed model combines the lightweight nature of MobileNetV2 with attention refinement mechanisms to effectively identify and emphasize diagnostically important regions while learning both local and global discriminative features. A comparative evaluation is also conducted using the Xception model as a baseline architecture. Experimental findings indicate that the proposed attention-enhanced MobileNetV2 model delivers competitive, and in some cases improved, classification performance with lower computational requirements. Its lightweight design makes the framework particularly suitable for efficient and potentially real-time clinical implementation. The proposed approach can support pathologists in analyzing histopathological images, reduce diagnostic workload, and improve the consistency and reliability of colorectal cancer detection.
Sentiment Analysis For Cyberbulling Detection Using NLP And LSTM
The phenomenon of cyberbullying has emerged as a critical challenge in the digital landscape, posing detrimental effects on individuals and broader societal well-being. A practical solution to this widespread issue involves the accurate identification of cyberbullying within social media platforms, which constitute a significant share of digital communication. While traditional approaches have primarily utilized machine learning algorithms and pre-trained language models, these often face challenges such as high computational complexity and limited adaptability to nuanced linguistic patterns. This paper proposes an advanced framework that leverages Natural Language Processing (NLP) techniques combined with Long Short-Term Memory (LSTM) networks to improve cyberbullying detection in online text. The framework applies refined text preprocessing steps—such as tokenization, stopword removal, stemming, and lemmatization—to ensure high-quality and noise-free input data. Sentiment features and contextual patterns are extracted using embedding methods to preserve semantic information. These processed inputs are then fed into an LSTM model, which effectively captures the sequential and temporal dependencies in textual data, making it well-suited for understanding the dynamic nature of cyberbullying language. Additionally, to address class imbalance in the multi-class setting, resampling techniques are employed, improving the model's robustness without inducing bias. The proposed system demonstrates that combining deep learning with comprehensive NLP enhances the accuracy and contextual understanding required for effective cyberbullying detection.
Secure And Privacy Preserving IoT Cybersecurity With A Blockchain Based Zero Trust Model
This research proposes a Unified Quantum-Resilient Blockchain–Zero-Knowledge Proof Privacy Authentication Framework (QBC-ZKPAF) designed to strengthen security, privacy, and authentication in Internet of Things (IoT) environments. The proposed framework combines blockchain technology, Zero Trust Architecture (ZTA), post-quantum cryptography, and Zero-Knowledge Proofs (ZKPs) to provide secure communication, privacy-preserving authentication, and effective access control. A hybrid Reinforcement-Lattice Blockchain Key Generation mechanism is employed to generate quantum-resistant cryptographic keys, while a Deep Q-Network Multi-Factor Secure Key (DQN-MFSK) mechanism dynamically selects secure keys based on authentication requirements and network conditions. Zero-Knowledge Proof-based signatures further protect sensitive information by enabling authentication without revealing confidential data. The immutable blockchain ledger records device interactions, access requests, and data transactions in a tamper-resistant manner, thereby improving transparency, traceability, auditability, and post-event forensic analysis. In the event of suspicious activity or security breaches, the framework supports accurate source identification through a tracing key maintained by the audit server within the Zero Trust Architecture. By decentralizing identity management and incorporating multi-factor authentication, QBC-ZKPAF provides enhanced protection against conventional cyber threats as well as emerging quantum-based attacks. Experimental evaluation demonstrates promising performance, achieving 98% privacy preservation, 700 transactions per second (TPS), a quantum resilience score of 0.98, and 96% access control effectiveness. These results indicate that the proposed framework is a reliable and efficient solution for securing modern IoT and blockchain-based applications.
Secure Privacy-Preserving Search And Access Control For Encrypted Cloud Data
Cloud-assisted Internet of Things (IoT) applications require secure mechanisms for storing, sharing, and retrieving encrypted data. Key-aggregate searchable encryption is an effective approach that enables a cloud server to perform keyword searches over files encrypted under different public keys while allowing authorized users to use a single, constant-size aggregate secret key. However, many existing schemes remain vulnerable to keyword-guessing attacks, which can expose sensitive information contained in keyword ciphertexts and search trapdoors. This study proposes a secure and efficient key-aggregate searchable encryption scheme for cloud-assisted IoT environments. The proposed approach enables data owners to securely share selected encrypted documents with multiple authorized users while allowing users to delegate keyword-search operations to the cloud server without revealing the privacy of the underlying keywords or trapdoors. The security of the scheme is formally defined under the standard model and demonstrated against indistinguishable selective-file chosen-keyword attacks. A rigorous security proof is provided to validate the confidentiality and privacy guarantees of the proposed mechanism. Furthermore, theoretical analysis and experimental simulations demonstrate the efficiency, flexibility, and practical applicability of the scheme for secure data retrieval in cloud-assisted IoT systems.
Predicting Neck Motion Direction With Explainable AI
Head and neck injuries are major concerns in sports, healthcare, and automotive safety, making accurate prediction of neck movement during head impacts essential for injury prevention and safety enhancement. This study proposes an Explainable Artificial Intelligence (XAI)-based system for predicting neck movement direction using machine learning techniques, including Neural Networks and XGBoost, integrated with biomechanical sensor data. Experimental data representing mild head impacts, including flexion and lateral movements commonly observed in sports activities, are analyzed using OpenSim for biomechanical modeling and assessment. The proposed XGBoost classifier achieves 98% prediction accuracy, demonstrating its effectiveness in identifying neck movement directions. The key contribution of the system is the integration of Explainable AI, which provides transparent and interpretable insights into model predictions, supporting clinicians, safety engineers, and researchers in making informed decisions. The proposed approach has potential applications in sports injury prevention, athlete training, automotive safety design, clinical diagnosis, and rehabilitation. By combining machine learning, biomechanical analysis, sensor-based measurements, and explainability, the research provides a reliable and interpretable framework for neck movement prediction and contributes to the development of safer and more effective injury-prevention solutions.
Explainable Artificial Intelligence (XAI)
Securing IIoT Environments with Blockchain-Enabled End-to-End Protection
The rapid growth of the Industrial Internet of Things (IIoT) has significantly enhanced industrial efficiency, productivity, automation, and operational performance. However, conventional security frameworks based on static policies and centralized management are increasingly inadequate for the scale, heterogeneity, and dynamic nature of modern IIoT environments. These approaches face challenges related to scalability, data integrity, authentication, and adaptability, thereby increasing the vulnerability of industrial systems to cyberattacks. To address these limitations, this research proposes an innovative end-to-end security framework based on blockchain technology for IIoT environments. The proposed framework employs a decentralized architecture that eliminates dependence on a single central authority while providing secure authentication, authorization, and data integrity across distributed industrial networks. Smart contracts are incorporated to enable automated and dynamic enforcement of security policies, allowing rapid adaptation to emerging security threats. In addition, lightweight cryptographic mechanisms are utilized to reduce computational and communication overhead, making the framework suitable for resource-constrained IIoT devices. A hybrid blockchain architecture combining public and private blockchain characteristics is introduced to balance security, scalability, privacy, and performance. The framework further improves data efficiency by storing only essential information on the blockchain while maintaining reliable and verifiable records. Overall, the proposed approach provides a resilient, scalable, adaptive, and secure solution for protecting heterogeneous IIoT ecosystems against evolving cyber threats.
Copyright (c) 2026 Author https://creativecommons.org/licenses/by-nc-nd/4.0/
Compliance-Aware Conversational AI for Regulated Enterprise Decision Support
Conversational AI systems are now used in various settings to improve decision-making in different fields like business, healthcare, finance, human resources, customer services, etc. Though conversational AI systems hold many benefits for improving productivity, their integration into an organizational environment poses many challenges regarding how to maintain organizational Compliance. Ideal conversational AI systems are designed to maximize user experience or response accuracy; however, in many systems, regulatory requirements are often neglected during conventional design. This paper describes Compliance-Aware Conversational AI, a new Conversational AI system particularly designed for decision support in an enterprise environment. This system is equipped with a new decision support feature whereby decision output is checked for Compliance automatically, unlike other decision support systems in modern AI. This system features a hierarchical architecture in which high-level decision support is isolated from low-level language understanding and response generation. A new feature in Compliance-Aware Conversational AI is reward planning in decision support in enterprise decision support systems. It is evaluating the proposed approach with enterprise scenarios basing on the simulation: financial advisory support, human resources policy guidance, and data access decision assistance. Experimental results show that compliance-aware conversational agents strongly reduce policy violations while maintaining a high response relevance and user satisfaction. For the visualization of the accuracy of responses, adherence to the Compliance, and interaction latency, it shows a favorable trade-off compared to an unconstrained model for conversational interactions. These results also point towards the path of integrating compliance awareness into conversational AI as a necessary next step for its safe and scalable deployment in regulated enterprise environments.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by-nc-nd/4.0/
Digital Risk Twins: AI-Driven Simulation for Enterprise Compliance
In this paper, the author examines such a concept as Digital Risk Twins (DRTs) as driven by AI to address the facilities of the enterprise as they relate to compliance management in a dynamic regulatory landscape. DRTs simulate potential risks, vulnerabilities, and compliance gaps with the help of AI-based simulating virtual representation of the infrastructure of an organization. DRTs can in real time adapt to new regulatory changes and emergent threats through the application of machine learning (ML) and deep learning (DL). Various simulated situations prove the benefit of DRTs in such fields as GDPR compliance, monitoring financial transactions, and safe storage of health-related data. Even though issues such as data quality and computing power exist, DRTs provide a scalable, proactive compliance management solution.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
A Catboost-Driven Framework For Monthly Crude Oil Price Forecasting
Crude oil prices play a crucial role in the global economy, influencing energy markets, transportation costs, industrial production, and financial investments. Accurate forecasting of crude oil prices enables governments, policymakers, investors, and energy organizations to make informed strategic decisions under dynamic market conditions. This study presents a CatBoost-driven framework for forecasting monthly crude oil prices using historical market data collected from 1983 to 2025. The proposed framework incorporates comprehensive data preprocessing and feature engineering techniques, including lag variables, rolling mean, rolling standard deviation, seasonal encoding using sine and cosine transformations, and monthly price change indicators to effectively capture temporal dependencies and nonlinear market behavior. The CatBoost Regressor is employed due to its ability to model complex relationships while minimizing overfitting through ordered boosting. The performance of the proposed model is evaluated using Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R² Score). Experimental results demonstrate that the proposed framework achieves accurate and reliable forecasting performance with strong generalization capability and computational efficiency. The findings indicate that the CatBoost-based approach provides an effective and scalable solution for monthly crude oil price forecasting and can serve as a valuable decision-support tool for stakeholders in the energy and financial sectors.
Recursive Self-Optimizing Neural Architectures for Ultra-High-Resolution Wind Speed Forecasting and Autonomous Grid-Stabilization Protocols Under Extreme Uncertainty
Wind power is inherently intermittent, and the value of any grid integration strategy is bounded by how well short horizon wind behaviour can be predicted and how safely that prediction is turned into an operating decision. This paper presents a recursive self-optimizing neural forecasting architecture, referred to as RSO-Net, for ultra-high-resolution wind speed prediction at sub-minute granularity, paired with a multi-agent software system that converts probabilistic forecasts into grid-stabilization recommendations under a mandatory human approval gate. The term recursive self-optimizing describes a bounded, logged, and reversible outer-loop controller that adjusts learning hyperparameters between short adaptation windows based on rolling validation performance; it does not describe, and this paper does not propose, a system that acts on the grid without human authorization. We detail the theoretical background of temporal convolution and self-attention based probabilistic forecasting, review the OpenAI Agents SDK as an example orchestration substrate, describe a six-agent architecture covering ingestion, anomaly detection, forecasting, knowledge retrieval, stabilization proposal, and human approval, and report experimental comparisons on synthetic high-resolution wind data against persistence, ARIMA, and standard LSTM baselines. The proposed RSO-Net achieves a root mean square error of 0.71 m/s versus 1.10 m/s for a standard LSTM, and simulated frequency response shows materially reduced deviation when agent proposed actions are reviewed and approved before dispatch. We conclude with a discussion of the safety, auditability, and human oversight properties that we consider prerequisites for any such system prior to field deployment.
Copyright (c) 2026 Author https://creativecommons.org/licenses/by-nc-nd/4.0
Autonomous IT Risk Mitigation Using Deep Reinforcement Learning in Cloud Environments
Cloud computing now lies at the heart of key enterprise IT, providing scalable, flexible management of resources. Yet, it's the very dynamism and inherent complexity of cloud setups that bring real operational risks in the form of security breaches, outages, and performance dips. Traditional mitigation-risk, through static rules, manual tweaks, or reactive monitoring, frequently fails to keep pace with fast-changing conditions and a growing scale. This paper develops an intelligent autonomous IT risk mitigation framework with deep reinforcement learning, motivated for cloud environments. It enables intelligent agents to continually monitor the states of the system, estimate risk status, and perform mitigation actions such as resource reallocation, access tightening, and workload throttling. Learning by interaction with the cloud to steer towards optimal policies, the agent proactively reduces risk exposure with no sacrifice of service performance. We ran simulation-based tests, that emulates enterprise-style cloud scenarios: time-varying workloads, security threats, and resource limitations. The results here demonstrate that DRL-driven mitigation significantly cuts down the number of risk incidents and response times compared to rule-based or purely reactive methods. We also provide easy-to-read, interpretable visuals of the tracking of risk reduction, policy convergence, and the trade-offs with performance. Real-time operation scenarios further illustrate the readiness of this approach for production use. In all, it can be inferred from the findings that autonomous mitigation guided by DRL represents a scalable and adaptive means of handling IT risk in cloud infrastructures, allowing organizations to move from reactive risk handling to proactive, self-optimizing cloud operations.
Policy-Constrained Neural Networks for Compliance-Aware Decision Support Systems
The enterprise decision provides a support system that enhances reliance on the neutral network, automating decision-making in sectors such as finance, healthcare, and even human resources. Although the models achieve strong predictive performance, they do so without explicit regard for regulations, legality, or internal policies. When a prediction is statistically accurate, a lack of consistency may pose compliance risks for model output, which may contradict business standards, regulatory requirements, and even ethical guidelines. A regulatory examination of the AI-driven decision may enhance an enterprise's obligations to ensure compliance, which is directly within a model for deception procedures. This paper presents a policy-constrained neural network framework that ensures compliance and enables the system to make decisions. The framework provides the implementation of formalized policies derived from regulations, organizational norms, or even governance standards. This can directly interfere with the training process of network neutrals. In reality, by combining a constraint-aware loss function with rule-based penalties, an approach can be proposed that yields model outputs that adhere to the predicted compliance boundaries while preserving predictive efficacy. The simulation-based experiments will be carried out using an enterprise-inspired decision dataset to make the policy limitations clear. The results show that the policy-constraint model dramatically reduces compliance violations compared to an untrained neural network, with minimal impact on overall accuracy. Illustrating how policy integration shapes model behaviour will lead to the discovery of a policy-contained neural network that provides a viable pathway. The approach will deliver AI-driven decisions aligned with the company and that demonstrate compliance standards, while also facilitating accountable, trustworthy decision automation.
IoT Based Air Quality Monitoring And Air Purifier System
Air pollution has become a serious environmental and health concern worldwide, especially in urban and industrial regions. This project presents an IoT-based air quality monitoring and air purifier system designed to detect harmful gases and improve indoor air conditions. The system uses a Raspberry Pi Pico W microcontroller with built-in Wi-Fi for real-time monitoring and cloud connectivity. Sensors such as MQ135, MQ7, MQ2, and DHT11 are used to measure gas concentration, temperature, and humidity. When pollutant levels exceed a predefined threshold, the system automatically activates a purifier using UV LEDs and a fan. Data is displayed on an OLED screen and uploaded to the cloud for remote monitoring via ThingSpeak. It provides real-time alerts and ensures better air quality management.
Intellectual Zigbee Supported Wireless Sensor Network For Landslide Supervision System
The Landslides are catastrophic natural events that position important hazards to social lives, structure, and the atmosphere. Early detection and monitoring of landslide- prone areas are essential to mitigate these risks. This paper presents a Landslide Finding and Observing System (LFOS) using Wireless Sensor Networks (WSNs) and the Zigbee communication protocol. The proposed system leverages a grid arrayed with sensor nodes of landslide- prone areas to monitor conservational factors such as earth moistness, earth heaviness, vibration, hotness, and tilt— key indicators of potential landslides. Each sensor node collects real-time data and wirelessly transmits it to a central Zigbee Coordinator, which aggregates the data for further analysis. The processed data is sent to a Base Station for real-time monitoring, where threshold-based alerts are generated in case of abnormal readings, and triggering timely notifications to relevant authorities. The system utilizes Zigbee's low-power, short-range mesh network to ensure reliable data transmission over large, remote areas, while minimizing energy consumption. Through the integration of sensor data and wireless communication, this system offers an efficient, cost- effective for landslide early warning and monitoring.
IoT-Driven Safety And Environmental Monitoring System For Coal Mining Workers
The indispensable task of ensuring the safety of workers in coal mining is a crucial issue because of dangerous underground conditions such as toxic gas emissions, high temperatures and humidity, structural vibrations, etc. However, traditional monitoring technologies do not support real-time transmission, long-distance communication, and energy-efficient requirements, which cannot meet the needs of underground mining. In overcoming these shortcomings, this article proposes an IoT-based safety monitoring system equipped with Zigbee hardware, able to deliver continuous real-time monitoring of miners' environmental and physiological conditions. This contains three sensors; DHT11 (temperature & humidity), MQ2 (gas detection), SW-420 (vibration monitoring), and pulse sensor (heart rate tracking). These sensors are incorporated into a wearable device connected to a centralized control hub through Zigbee, ensuring long-distance, low-power, and anti-interference communication in underground scenarios. The data is uploaded to ThingSpeak, a cloud-based IoT analytics platform and real-time visualization, anomaly detection and historical trend analysis is done on the uploaded data. All sensor information is processed within the control unit and alert users as soon as possible as long as a measured parameter exceeds pre-defined limits. Also, the system can also track location in real time, helping emergency responders act quickly and rescue. Experimental results show that the system detects environmental hazards and worker health and anomalies, with low latency and high accuracy, with respect to the data transmission. This system improves the safety of workers, efficiency in operations, and preparedness for emergencies in the mining space by using the combination of IoT, Zigbee, and cloud-based analytics. We plan to build upon these results with AI-based predictive analytics and edge computing for optimum performance and scalability.
Real-Time Parking Slot Detection And Enhanced Security System For Public Parking Using IoT
The rapid growth of urbanization and vehicle ownership has intensified parking-related challenges in cities, leading to traffic congestion, increased fuel consumption, and inefficient utilization of parking spaces. This paper presents a comprehensive Internet of Things (IoT)-based smart parking system designed to address these challenges through real-time parking slot monitoring, cloud-based data management, and enhanced security mechanisms. The proposed system employs IoT sensors to detect parking slot occupancy and transmits the collected information to a cloud server, enabling users to monitor parking availability through a mobile application. Additionally, the system integrates surveillance cameras, automated alerts, and access control mechanisms to improve parking security and safety. By providing real-time updates and intelligent monitoring, the proposed solution minimizes search time for parking spaces, reduces traffic congestion, improves resource utilization, and enhances the overall user experience. The integration of cloud computing with IoT technology ensures scalability, reliability, and efficient data management, making the system suitable for deployment in smart cities and modern urban environments.
Real-Time LPG Leakage Monitoring System Using IoT And Cloud Technology
The explosive nature of liquefied petroleum gas (LPG) and the frequent fatalities related with its usage as a cooking fuel have raised safety issues in developing countries. Consequently, there is a rising emphasis on using Internet of Things technology to address these concerns. In this regard, this study proposes a real time IoT-cloud based leakage monitoring system to enhance safety. The system uses hybrid synergetic combination of Internet of Things and cloud technologies to improve real-time tracking of LPG information. This platform enables remote monitoring of real time information on LPG variables such as gas leakage, temperature, humidity, as well as user access to graphical pictures and numerical data. To address the shortcomings of existing LPG leakage monitoring systems, the model includes novel features such as real-time SMS updates, alarm notification. In terms of system cost, network connectivity, technology employed, model sensitivity, and gas leak detection range, the proposed system outperforms existing models.
A Comparative Study of Generative AI Models in Semantic Communication Networks
Communication networks are gradually shifting from traditional bit-oriented transmission toward semantic communication, where the emphasis is placed on delivering meaningful and context-aware information rather than transmitting complete data packets. This transition has encouraged the adoption of Generative Artificial Intelligence (Generative AI) models to improve semantic understanding, intelligent encoding, and adaptive information delivery. The present study provides a comparative examination of prominent generative models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), Transformer architectures, Large Language Models (LLMs), and Diffusion Models within semantic communication networks. The comparison focuses on important performance aspects such as semantic accuracy, computational efficiency, latency, bandwidth optimization, contextual understanding, and adaptability in dynamic communication environments. In addition, the study discusses the applicability of these models in emerging technologies such as 5G, 6G, Internet of Things (IoT), edge intelligence, and autonomous communication systems. Particular attention is given to practical concerns, including privacy preservation, semantic ambiguity, computational cost, scalability, and security challenges associated with generative AI integration. Based on the comparative analysis, Transformer-based and large language models demonstrate stronger capabilities in semantic representation and intelligent communication, whereas hybrid generative frameworks show promising potential for improving efficiency and contextual accuracy in future communication ecosystems. The study concludes that Generative AI can significantly contribute to the development of intelligent, adaptive, and semantically aware communication infrastructures.
Generative Artificial Intelligence (Generative AI)
Blockchain and Artificial Intelligence Integration for Secure Communication Networks: A Study
Modern communication networks handle enormous volumes of sensitive information across cloud platforms, mobile devices, smart systems, and interconnected infrastructures. As these networks become more intelligent and decentralized, concerns related to cyberattacks, unauthorized access, data tampering, and privacy violations continue to grow. Conventional security mechanisms often face limitations in addressing dynamic and large-scale communication environments where trust, transparency, and automated decision-making are equally important. In this context, the integration of Blockchain and Artificial Intelligence (AI) has gained considerable attention as a promising approach for strengthening secure communication networks. Blockchain contributes decentralized trust, immutability, and secure data management, while AI enables intelligent threat detection, predictive analysis, anomaly identification, and automated security responses. This study explores how the combined use of these technologies can improve network protection, optimize communication reliability, and support secure information exchange in next-generation communication systems. Particular attention is given to applications in Internet of Things (IoT), fifth-generation (5G) and sixth-generation (6G) networks, smart cities, edge computing, and autonomous systems. The study also examines practical challenges such as computational overhead, scalability constraints, interoperability issues, energy consumption, privacy risks, and regulatory concerns that may influence large-scale implementation. By reviewing existing developments and emerging research directions, the paper demonstrates that Blockchain–AI integration offers a strong foundation for building resilient, intelligent, and trustworthy communication ecosystems capable of addressing evolving cybersecurity challenges.
Ultrasonic Sensor-Enabled Rover For Accurate Urban Building Height Measurement
A significant proportion of mobile surveying systems in the market are not economically viable due to their inconvenience in operation such as large size, complex data processing requirements and limited accessibility to areas without vehicle access. This paper targets to design a model to implement a rover-based system to highlight the challenges that includes the measure the height of the building or any object in urban environments using a low- cost. The other advantage is that the assembling of system is easy as it consists of ultrasonic sensors, stepper motors and a laser system. Here, it integrates calibration mechanisms to be highly accurate, and the rover's modular architecture promotes robustness and easy replication for multiple scenarios. This advances the measurement of building heights in urban settings by enhancing users’ control of the process, and in delivering results that reflect local requirements.
Calmnest: IOT Based Smart Cradle Using Controller Ble Sense
This paper presents CalmNest, an IoT-based smart cradle designed to enhance infant safety and comfort using a microcontroller integrated with BLE Sense technology. The system monitors key parameters such as motion, sound (cry detection), temperature, and humidity in real time. Upon detecting abnormal conditions, the cradle automatically performs soothing actions, including controlled rocking, and sends alerts to caregivers via a mobile interface. The use of Bluetooth Low Energy (BLE) ensures low power consumption and efficient wireless communication. The proposed system offers a reliable, cost-effective, and user-friendly solution for remote infant monitoring. Experimental results demonstrate improved responsiveness and reduced caregiver intervention, highlighting the potential of IoT-enabled smart childcare systems.
AI-Driven Traffic Prediction and Congestion Control in Smart Communication Systems
The rapid expansion of smart communication systems, supported by advanced technologies such as fifth-generation (5G), sixth-generation (6G), Internet of Things (IoT), edge computing, cloud networking, and intelligent transportation infrastructures, has significantly increased network traffic complexity and communication demands. Traditional traffic prediction and congestion control mechanisms often struggle to efficiently manage dynamic traffic patterns, high mobility, massive device connectivity, and real-time service requirements in modern communication environments. Artificial Intelligence (AI)-driven traffic prediction and congestion control have emerged as effective solutions for enhancing network performance through intelligent analytics, adaptive learning, and autonomous decision-making capabilities. This study investigates the application of AI techniques, including Machine Learning (ML), Deep Learning (DL), Reinforcement Learning (RL), Neural Networks, and predictive analytics, for traffic forecasting and congestion management in smart communication systems. The research explores how AI-enabled models improve traffic prediction accuracy, optimize routing strategies, reduce packet loss, minimize communication delays, and enhance Quality of Service (QoS) across heterogeneous communication infrastructures. Furthermore, the study examines key challenges associated with AI-driven traffic management, including data heterogeneity, computational complexity, scalability, security risks, privacy concerns, and real-time processing limitations. By analyzing recent developments and intelligent networking strategies, the paper highlights the transformative role of AI in improving traffic flow optimization, network resilience, congestion avoidance, and intelligent resource allocation in smart communication ecosystems. The findings indicate that AI-powered traffic prediction and congestion control frameworks significantly enhance communication efficiency, reduce latency, improve bandwidth utilization, and support the development of autonomous and intelligent communication systems for future smart cities and connected environments.
Machine Learning Techniques for Energy-Efficient Routing in IoT Communication Networks
The rapid proliferation of the Internet of Things (IoT) has transformed modern communication infrastructures by enabling seamless connectivity among heterogeneous smart devices, sensors, and autonomous systems. However, the limited energy resources of IoT devices and the increasing demand for scalable, reliable, and low-latency communication have created significant challenges for routing and network sustainability. Traditional routing protocols often struggle to optimize energy consumption while maintaining network performance in dynamic and resource-constrained IoT environments. Machine Learning (ML) techniques have emerged as promising solutions for enabling intelligent, adaptive, and energy-efficient routing in IoT communication networks through predictive analysis, autonomous decision-making, and real-time optimization. This study investigates the role of machine learning techniques, including supervised learning, unsupervised learning, reinforcement learning, deep learning, and hybrid optimization models, in enhancing routing efficiency and minimizing energy consumption in IoT communication networks. The research examines how ML-based routing frameworks improve packet delivery ratio, reduce communication overhead, optimize path selection, prolong network lifetime, and enhance Quality of Service (QoS) in heterogeneous IoT ecosystems. Furthermore, the study explores major challenges affecting ML-enabled energy-efficient routing, including resource limitations, computational complexity, security vulnerabilities, scalability issues, data heterogeneity, and model adaptability in dynamic networking environments. By analyzing recent advancements and intelligent routing mechanisms, the paper highlights the transformative potential of machine learning in developing sustainable, autonomous, and energy-aware IoT communication infrastructures. The findings indicate that ML-driven routing techniques significantly improve energy efficiency, network reliability, and communication performance, thereby supporting the development of scalable smart city applications, industrial automation, healthcare systems, and intelligent environmental monitoring networks.
Deep Learning Approaches for Intrusion Detection in Intelligent Communication Networks; A Study
The increasing integration of intelligent communication networks, including fifth-generation (5G), sixth-generation (6G), Internet of Things (IoT), cloud computing, edge intelligence, and software-defined networking, has significantly improved communication efficiency and connectivity. However, the rapid expansion of these advanced network infrastructures has also increased vulnerability to sophisticated cyberattacks, malicious intrusions, and security breaches. Traditional intrusion detection systems (IDS), which primarily rely on signature-based and rule-based mechanisms, often fail to identify emerging and complex threats in dynamic communication environments. Deep Learning (DL) approaches have emerged as effective solutions for intrusion detection due to their ability to automatically extract hidden patterns, analyze high-dimensional network traffic data, and detect anomalous behavior in real time. This study explores various deep learning techniques, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Autoencoders, Deep Belief Networks (DBN), and hybrid deep learning models for intrusion detection in intelligent communication networks. The paper examines the role of deep learning in enhancing attack detection accuracy, minimizing false alarm rates, improving scalability, and enabling adaptive threat intelligence in heterogeneous networking environments. Additionally, the research discusses challenges such as data imbalance, adversarial attacks, computational overhead, privacy concerns, and model interpretability in intelligent network security systems. By analyzing recent advancements and research developments, this study highlights the transformative potential of deep learning-based intrusion detection systems in strengthening cybersecurity frameworks for future intelligent communication networks. The findings demonstrate that deep learning techniques significantly improve intrusion detection performance, network resilience, and automated security response mechanisms, contributing to safer and more reliable communication infrastructures.
Artificial Intelligence-Based Dynamic Spectrum Allocation for 6G Wireless Communication Networks
The rapid evolution of wireless communication technologies and the anticipated deployment of sixth-generation (6G) networks demand highly efficient spectrum utilization techniques to support ultra-high data rates, massive connectivity, ultra-low latency, and intelligent communication services. Traditional static and semi-dynamic spectrum allocation approaches are insufficient for addressing the increasing complexity, heterogeneity, and spectrum scarcity challenges associated with 6G wireless communication environments. Artificial Intelligence (AI)-based Dynamic Spectrum Allocation (DSA) has emerged as a promising solution for optimizing spectrum management through intelligent decision-making, adaptive learning, and real-time resource allocation. This study explores the integration of AI techniques, including machine learning, deep learning, reinforcement learning, and cognitive radio systems, in enabling dynamic spectrum allocation for 6G wireless communication networks. The research investigates how AI-driven spectrum sensing, prediction, interference mitigation, and autonomous resource management improve network efficiency, reliability, and Quality of Service (QoS). Furthermore, the study examines challenges such as spectrum congestion, security vulnerabilities, computational complexity, energy consumption, and regulatory concerns affecting AI-enabled spectrum allocation systems. By analyzing recent developments and technological advancements, the paper highlights the transformative role of AI in enhancing spectral efficiency, reducing latency, improving network adaptability, and facilitating intelligent communication ecosystems in 6G environments. The findings indicate that AI-powered DSA frameworks can significantly improve spectrum utilization while ensuring robust, scalable, and energy-efficient wireless communication infrastructures for future smart cities, autonomous systems, and Internet of Things (IoT) applications.
ChatGPT-Assisted Network Optimization: Leveraging Large Language Models for Adaptive Communication Protocols
The evolution of communication networks has witnessed an increasing demand for intelligent, adaptive, and self-optimizing protocols to handle dynamic data flow and user needs. This study investigates the integration of ChatGPT and other large language models (LLMs) into network optimization frameworks, enabling context-aware decision-making, predictive resource allocation, and real-time traffic management. By simulating multi-agent communication systems supported by LLM-based controllers, the research demonstrates improved throughput, latency reduction, and enhanced fault tolerance in adaptive protocol environments. Results indicate that LLM-assisted optimization can increase network efficiency by up to 22% compared to traditional heuristic-based models. The study further explores ethical and computational implications, highlighting the potential of AI-driven natural language models to revolutionize adaptive communication systems in 5G, 6G, and IoT networks.
Copyright (c) 2025 Author https://creativecommons.org/licenses/by-nc-nd/4.0
Intelligent Intrusion Detection Systems for Secure Communication in Next-Generation Networks
As communication technologies evolve toward next-generation networks (5G and beyond), ensuring robust security has become a critical challenge. Traditional intrusion detection systems (IDS) often struggle to cope with the massive data volume, dynamic architecture, and heterogeneous nature of these networks. This research paper presents an in-depth analysis of Intelligent Intrusion Detection Systems (IIDS) that leverage Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) techniques to detect, classify, and prevent cyber threats in real-time. The study evaluates various ML algorithms such as Support Vector Machines (SVM), Random Forest (RF), and Deep Neural Networks (DNN), using benchmark datasets like NSL-KDD and CICIDS2017. Results indicate that AI-based IDS models outperform conventional systems in terms of detection accuracy, adaptability, and latency reduction. The paper concludes by proposing a hybrid AI-driven IDS framework optimized for next-generation networks (NGNs), emphasizing scalability, automation, and low false alarm rates.
Federated Learning in Edge Communication Systems: Enhancing Privacy and Efficiency in IoT Networks
The rapid evolution of the Internet of Things (IoT) has led to the generation of vast amounts of sensitive data, driving the need for efficient and privacy-preserving machine learning models. Federated Learning (FL) has emerged as a transformative approach that allows decentralized model training across multiple edge devices without sharing raw data. This paper explores the integration of FL in edge communication systems, emphasizing its role in enhancing privacy, bandwidth optimization, and computational efficiency in IoT networks. The research investigates key components, architectures, and protocols of FL, analyzes performance metrics, and compares centralized versus federated frameworks through case-based evaluations. The study concludes that FL, coupled with edge computing, presents a sustainable paradigm for data-driven intelligence in IoT, balancing data security, latency reduction, and real-time adaptability.
Reinforcement Learning for Dynamic Spectrum Management in Cognitive Radio Networks
Dynamic Spectrum Management (DSM) is essential for optimizing the utilization of limited radio spectrum resources in next-generation wireless communication systems. Cognitive Radio Networks (CRNs) have emerged as a promising solution to address spectrum scarcity by allowing unlicensed users to access underutilized frequency bands dynamically. This research paper explores the application of Reinforcement Learning (RL) algorithms in achieving intelligent and adaptive spectrum allocation in CRNs. By employing model-free learning approaches such as Q-learning, Deep Q-Networks (DQN), and Multi-Agent Reinforcement Learning (MARL), CRNs can autonomously make real-time decisions on channel selection and power allocation. The paper presents a detailed analysis of RL-based DSM models, evaluates their performance in terms of throughput, latency, and spectral efficiency, and compares them with traditional optimization approaches. Results indicate that RL-driven CRNs achieve a 35–50% improvement in spectrum utilization and reduced interference levels, leading to enhanced network efficiency and stability.