Volume 16, Issue 2, June 2025

Transforming Agriculture Sustainability Through IoT-Powered Vertical Farming

Waleed Tariq, Haseeb Hassan, Junaid Arshad

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

Abstract- This study presents an advanced Internet of Things (IoT)-enabled vertical farming system that aims to transform urban sustainable agriculture. The system incorporates web- based technologies for remote crop monitoring and precise environmental data management. Amongst the main features of this device is the optimized soil moisture, effective use of rain water, and controlling temperature and humidity. Data visualization and management are made via ThingSpeak, an essential component of the system. The paper goes into detail about the criteria for selecting high-accuracy sensors and components, highlighting their cost-effectiveness and precision.

Keywords- Internet of Things, Vertical Farming, Urban Farming and Environmental Monitoring

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Identifying Risk Contributors in Requirements Engineering: A Comprehensive Survey

Mohamed Masoor, Hamed Jaferi

Faculty of Computer Science, International University of Science and Technology, Sudan

Abstract- It is a well-established fact that one of the worst scenarios for a software development team is when the requirements gathered during the requirement engineering phase fail to fully capture the customer’s needs. This misalignment can steer the development process in the wrong direction, turning a potentially successful project into a failure. To avoid such outcomes, it is crucial to identify not only the risks involved but also the underlying factors contributing to them. This study adopts a secondary research methodology to conduct a literature survey aimed at identifying common risks in the software requirements engineering phase. The resulting list of risk factors will serve as a valuable resource, particularly for inexperienced requirement engineers working in either research or the commercial software industry.

Keywords- Risk Management, Requirement Engineering, Software Industry and Research Methodology

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A Review of AI-Driven Intelligent Web Services: Techniques, Applications, and Quality of Service Enhancements

Muhammad Mohsin Shafqat

University of Engineering and Technology, UET, Lahore-Pakistan

Abstract- The integration of Artificial Intelligence (AI) into web services has triggered a significant evolution, enabling dynamic, scalable, personalized, and intelligent service delivery. Intelligent Web Services combine machine learning, semantic web technologies, and decision-making models to optimize Quality of Service (QoS) parameters, automate processes, and enhance user engagement across domains such as healthcare, cloud computing, and library information systems. This review paper systematically analyzes developments between 2020 and 2025, highlighting key AI-based architectures, semantic technologies, QoS optimization strategies, cross-domain applications, and ethical challenges. It identifies existing gaps, compares methodologies, and provides future directions to support the design of trustworthy and adaptive intelligent web services.

Keywords- Artificial Intelligence, Intelligent Web Services, Quality of Service, Cloud Computing, Semantic Web, Explainable AI, Neutrosophic Logic, Ethical AI and Cross-Domain Applications

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A Review of Machine Learning and Deep Learning Techniques for DDoS Attack Detection in IoT Networks

Aqsa Afzal, M. Junaid

IDS, University of Engineering and Technology (UET), Lahore

Abstract- The swift proliferation of the Internet of Things (IoTs) resulted in increased vulnerabilities, with Distributed Denial of Service (DDoS) threats emerging as one of the significant critical attacks. These assaults disrupt services by overwhelming IoT networks, exploiting the restricted security and computational capabilities of linked devices. This paper offers a systematic assessment of current advancements in DDoS attack detection through Deep Learning (DL) and Machine Learning (ML) models, with a particular emphasis on the CICIoT2023 dataset. In addition, it provides a comparison of widely used data sets for DDoS detection, highlighting their characteristics and applicability in different research scenarios. The review explores various types of DDoS assaults, including protocol-based, volume-based, and application layer-based attacks, emphasizing their impact on IoT infrastructures. Furthermore, the cuttingedge ML and DL algorithms are compared based on their detection accuracy and efficiency, with models like XGBoost, Convolutional Neural Networks (CNNs) and Random Forest (RF) demonstrating superior performance on the CICIoT2023 dataset. Challenges such as data imbalance, lack of standardized datasets, and evolving attack strategies are discussed, with potential future directions, including the integration of blockchain, edge computing, and adversarial resilience. This review underscores the need for robust, expandable, and flexible DDoS identification mechanisms to secure the growing landscape of IoT networks.

Keywords- Internet of Things (IoTs), Distributed Denial of Service (DDoS), Machine Learning (ML), Deep Learning (DL), Random Forest (RF), Convolutional Neural Network (CNN) and CICIoT2023 Dataset

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Enhancing IoT Connectivity: A Comparative Study of Wireless Protocols for Performance and Efficiency

Anam Suleman, Itrat Fatima, Junaid Arshad

CS Department, University of Engineering and Technology, Lahore

Abstract- The Internet of Things (IoT) ecosystem relies heavily on efficient and reliable wireless communication protocols to enable seamless connectivity across a vast number of heterogeneous devices. With diverse requirements ranging from low latency to ultra-low power consumption and extensive range, choosing the optimal protocol is critical. This paper compares five widely used wireless protocols—Wi-Fi, Bluetooth Low Energy (BLE), Zigbee, Long Range (LoRa), and Narrowband IoT (NB-IoT)—based on power consumption, latency, data rate, range, and scalability. Our analysis draws from empirical studies, real-world deployments, and simulation data to evaluate performance across various IoT use cases. The findings aim to guide researchers and developers in selecting appropriate technologies for specific applications, enhancing the efficiency of IoT systems.

Keywords- Internet of Things (IoT), Wireless Communication, Wireless Protocols, IoT Connectivity, Performance Evaluation, Energy Efficiency

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Exploring IoT-Driven Approaches for Water Quality Monitoring: A Systematic Review

Muhammad Usama Ashraf, M.J. Arshad

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

Abstract- Access to clean and safe water is increasingly under threat due to rapid industrialization, urbanization, and climate change, making effective water quality monitoring more critical than ever. Traditional methods, while reliable, are often labor-intensive, costly, and geographically limited. Recent advancements in Internet of Things (IoT) technologies offer promising solutions by enabling real-time, remote, and accurate monitoring of water resources. This systematic review explores the current landscape of IoT-driven water quality monitoring systems, examining various technological integrations such as machine learning models, optimization algorithms, and cloud computing. The analysis reveals that while basic IoT systems provide affordability and ease of deployment, integrating advanced techniques like machine learning and Quantum Approximate Optimization Algorithms (QAOA) significantly enhances performance and decision-making capabilities. Despite notable progress, challenges related to energy efficiency, data security, scalability, and sensor durability remain. Key research gaps include the lack of standardized protocols, limited long-term field studies, and underutilization of edge computing technologies. This paper provides a comprehensive overview of the advancements, challenges, and future directions of IoT-based water quality monitoring systems, offering valuable insights for researchers, industry professionals, and policymakers committed to improving global water sustainability.

Keywords- Water Quality, Internet of Things, Policymakers, Monitoring System and Challenges

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