International Journal of Artificial Intelligence and Communication Networks
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.
Copyright (c) 2026 Authors https://creativecommons.org/licenses/by-nc-nd/4.0
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.
Copyright (c) 2026 Author https://creativecommons.org/licenses/by-nc-nd/4.0
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.
AI-Powered 6G Networks: Enabling Ultra-Low Latency and Intelligent Resource Allocation
The evolution from 5G to 6G represents a paradigm shift in wireless communication, integrating artificial intelligence (AI) as a core enabler of ultra-fast, self-optimizing, and context-aware networks. Unlike 5G, which primarily emphasizes enhanced mobile broadband and massive connectivity, 6G aims to achieve ultra-low latency (<0.1 ms), terabit-per-second data rates, and intelligent resource orchestration across heterogeneous infrastructures. This research paper explores how AI-driven mechanisms—such as deep reinforcement learning (DRL), federated learning (FL), and graph neural networks (GNNs)—can optimize resource allocation, improve network adaptability, and ensure Quality of Service (QoS) in dynamic environments. Through simulation-based experiments, results show that AI-powered 6G models can improve spectral efficiency by 42%, reduce latency by 65%, and enhance energy utilization by 38% compared to conventional 5G systems. This study underscores AI’s transformative role in realizing fully autonomous, intelligent 6G networks for next-generation digital ecosystems.