Volume 17, Issue 1, February 2026

Traffic Prioritization and QoS Management for IoT Devices in Heterogeneous Networks

Faiz ul Hassan

University of Engineering and Technology, Lahore, Pakistan

Abstract - Traditional network infrastructures face serious threats due to the Internet of Things (IoT) increasing expansion in campus contexts. IoT devices frequently coexist with traditional consumer devices, vying for scarce network resources and bandwidth. IoT communications are susceptible to congestion, latency, and reliability problems in the absence of appropriate traffic prioritization, which can impair the functionality of vital applications like environmental monitoring, attendance systems, and surveillance. The traffic prioritization and QoS management techniques designed for heterogeneous campus networks with diverse IoT endpoints are examined in this study. This paper propose a lightweight, deployable architecture that classifies IoT flows at the edge using efficient feature-based classifiers, maps classes to DSCP and queueing policies enforced by access points and campus switches, and edge preprocessing to reduce congestion load. A modest campus testbed and Mininet/NS-3 simulation are part of the evaluation plans. While total upstream traffic volume decreases due to edge aggregation, we expect significant tail latency reductions for prioritized flows. An evaluation approach for campus-scale mixed traffic, an edge-classification profile for limited gateways, and a useful DSCP mapping strategy are important contributions.

Index Terms - Internet of Things (IoT), Quality of Service (QoS), Differentiated Services Code Point (DSCP), VLAN, Network Performance and Bandwidth Management

Cybercrime in Uzbekistan: Current Trends, Challenges, and Future Countermeasures

Pages: 5-12 (8) | [Full Text] PDF (363K)

Shirin Komilova, Feruza Abdumannobova, Debasis Das

Computer Science Department, Webster University in Tashkent, Uzbekistan

Abstract - The Republic of Uzbekistan is at a focal point in the digital era: the rapidity of technological adoption, the growing internet penetration, and the alteration in the economic facets place the nation in the spotlight of the prospects of digital transformation alongside the emergence of novel risks linked to the dynamic evolvement of cybercrime. Over the past few years, cybercrime in Uzbekistan has reached a critical peak of incidences with the number of cases of cyberattacks, financial fraud, phishing, ransomware, mobile malware and social engineering attacks on the rise. The increased infiltration of internet services, the spread of e-commerce, and the fast integration of the digital payment system have exposed individuals and organizations to cyber threats. The paper relies on the quantitative data reported by law enforcement agencies across the country, cybersecurity agencies, and international organizations and is supported by the qualitative data collected through interviews with IT specialists, government representatives, and victimized businesses. The present paper gives a detailed account of different kinds, origins, consequences and legal provisions of cybercrime in Uzbekistan. It also underscores the problems of law enforcement agencies and gives useful solutions as to how to react to cyber threats without compromising privacy and security.

Index Terms - Cybercrime, Cybersecurity, Uzbekistan, Digital Transformation, Cyber Policy and Central Asia

Toward Self-Sensing Roads: A Systematic Review of Embedded IoT Sensors for Pavement Monitoring and Maintenance

Pages: 13-20 (8) | [Full Text] PDF (524K)

Maryam Nasir Suleman, Abdurrehman, Junaid Arshad

Department of Computer Science University of Engineering and Technology, Lahore, Pakistan

Abstract - Recent advances in smart materials and sensing technologies have enabled pavements to evolve from passive structural elements into self-sensing infrastructure capable of monitoring traffic loads, structural condition, and environmental effects in real time. A growing body of research has explored conductive materials, embedded sensors, and self-sensing mechanisms for pavement monitoring, while parallel efforts have examined IoT-based data acquisition and analytics frameworks. However, existing review studies often address these topics from isolated perspectives, with limited integration between material-level sensing behavior, underlying self-sensing mechanisms, and system-level IoT architectures. This paper presents a comprehensive review of self-sensing road technologies by synthesizing insights from existing review literature and representative experimental studies. The review first summarizes key embedded sensing approaches and smart materials used in pavements, followed by an in-depth discussion of self-sensing mechanisms related to strain, damage, temperature, and moisture monitoring. Building on this foundation, the paper examines IoT architectures and system-level considerations required to support continuous data acquisition, communication, and integration with pavement management systems. Key challenges related to durability, scalability, power management, data interpretation, and long-term deployment are identified, and future research directions are outlined. By bridging material-level innovations with system-level IoT perspectives, this review provides a unified framework for understanding the current state and future potential of self-sensing pavements. The findings aim to support the development of resilient, intelligent, and sustainable road infrastructure capable of meeting the demands of modern transportation networks.

Index Terms - Smart Pavements, Self-Sensing Materials, Embedded IoT Sensors, Real-Time Road Monitoring, Piezoresistive Asphalt, Fiber-Optic Sensing, Carbon Nanotubes, Graphene Nanoplatelets and Pavement Health Monitoring

An Intelligent Conversational Agent Leveraging Dynamic Ontologies in Heterogeneous Computing

Pages: 21-25 (5) | [Full Text] PDF (316K)

Babatunde Damilola, Chinara Adebayo

Department of IT, University of Science and Technology, Nigeria

Abstract - By recognizing relationships among concepts during conversations and leveraging previously acquired knowledge, ontology-based chatbots are able to maintain conversational context. Although such agents can adapt to changing environments, they may still exhibit anomalous behavior under hostile conditions. These inconsistencies can result in suboptimal action planning and ineffective adaptation. To address this limitation, we propose a dynamic ontology-based conversational model that determines the most appropriate response by utilizing prior experience and a structured knowledge base, thereby enhancing action planning and adaptability in adverse scenarios. As emotions play a vital role in human interaction, our approach represents the emotional states of conversational agents in a more expressive manner rather than relying solely on emojis. Consequently, this work demonstrates a conversational agent that operates in a more lifelike and human-centric way.

Index Terms - Emotion Modeling, Affective Computing, Human–Computer Interaction and Lifelike Conversational Agents

Performance-Optimization and Privacy-Preservation Using Federated Learning for IoT Anomaly Detection: A Structured Review

Pages: 26-31 (6) | [Full Text] PDF (378K)

Marriam Salman, M. Junaid Arshad

Department of Computer Science, University of Engineering & Technology, Lahore, Pakistan

Abstract - The rapid rise of the Internet of Things (IoT) has been a major factor in the demand for detection mechanisms that are not only powerful but also ensure the privacy of users. The conventional models that require raw device data to be continuously sent to them suffer from scalability issues, high demand for bandwidth, and most importantly, high privacy risks that come with the continuous transfer of raw data. FL has emerged as the distributed alternative to collaborative model training thus reducing communication costs and at the same time increasing privacy. Among the recent advances, the application of structured sparsity, hierarchical aggregation, encrypted model update, adaptive client selection, optimization-based ensembles can effectively increase the accuracy of detection and at the same time preserve efficiency in IoT networks. They have given structured reviews along with the combination of these advances, how they are viewed and how they are described, pointing out their strengths and weaknesses simultaneously. The review presents very useful insights for the making of FL-based IoT anomaly detection systems that are not only very effective but also extremely robust in terms of data confidentiality against large scale distributions.

Index Terms - Federated Learning, Anomaly Detection, Privacy Preservation, Distributed Machine Learning, Edge Computing and Intrusion Detection

Integration of IoT with Artificial Intelligence

Pages: 32-36 (5) | [Full Text] PDF (389K)

Zaviyar Hasnain Bhutta

Department of Computer Science, University of Engineering and Technology, Lahore

Abstract - One of the most transformative trends in contemporary technology is the combination of Artificial Intelligence (AI) and the Internet of Things (IoT). The Internet of Things (IoT) enables billions of physical devices worldwide to collect and exchange data, and artificial intelligence provides the intelligence necessary to analyze, interpret, and act on that data. The efficiency, responsiveness, and intelligence of connected systems are enhanced by this fusion, which is commonly referred to as the Artificial Intelligence of Things (AIoT). AIoT systems are able to predict, learn from real-time data, and carry out autonomous tasks without the need for human intervention. This combination can be utilized in a variety of fields, including healthcare, agriculture, transportation, smart homes, and industrial automation. Other examples include However, the integration also results in issues with data security, interoperability, and system scalability. The benefits, applications, and repercussions of AI and IoT combined are the focus of this paper's investigation of their synergy.

Index Terms - Real-Time Data, Autonomous Systems, Predictive Analytics, Smart Systems, Healthcare Applications, Industrial Automation, Scalability, Interoperability, Data Security, Future Impact and Artificial Intelligence of Things (AIoT)

Volume 17, Issue 1, February 2026

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