International Journal of Mobile and Cloud Systems Engineering (e-ISSN: 3108-3315)

UAV-Enabled Smart Metering for Smart Cities: A Review

2026-01-17T09:09:23+00:00

With a focus on improving urban efficiency, sustainability, and human well-being, smart cities are rapidly emerging as a result of the convergence of Information and Communication Technologies (ICT) alongside the Internet of Things (IoT). One of the key drivers of a smart city is the concept of Smart Metering Infrastructure (SMI) as it provides intelligent data collection, monitoring, and control. This article covers the concepts of smart metering, its fundamental elements comprising Smart Meters (SMs), Data Management Centers (DMCs), and communication networks utilizing technologies such as Bluetooth, Zigbee, 6LoWPAN, Wi-Fi, and WiMAX, and its use in smart city applications. Security and privacy concerns are examined using the CIA triad framework (Confidentiality, Integrity, Availability), in addition to accountability requirements and policy implications for data protection. Furthermore, the research investigates the integration of Unmanned Aerial Vehicles (UAVs) for enhanced operational efficiency in smart city applications such as health monitoring, firefighting, Search and Rescue (SAR) operations, and surveillance. Performance analysis reveals that UAVs achieved detection rates exceeding 90% in search and rescue scenarios, while COVID-19 detection systems effectively covered 2 km² areas in approximately ten minutes. Despite these benefits, there are some limitations like high implementation costs, scalability, and data security that remain significant barriers to widespread adoption. The study concludes by identifying future research directions, including the development of adaptive regulatory frameworks, the enhancement of interoperability standards, and the establishment of robust security mechanisms to ensure the seamless deployment of smart metering systems in evolving urban environments.

Summarization of Legal Texts: A Review of Approaches, Challenges, and Applications in Judicial Analysis

2026-02-28T17:13:27+00:00

The rapid expansion of digital legal information, including court judgments, regulatory notices, and compliance documents, has increased the need for automatic summarization techniques in legal technology. Legal text summarization focuses on condensing extensive, domain-specific materials into concise, coherent, and legally accurate representations to assist judges, lawyers, and regulatory professionals in making faster and more informed decisions. However, the nature of legal writing, which involves specialized terminology, interconnected references, and context-dependent phrasing, presents challenges that are not commonly encountered in general-domain summarization. This review examines major advancements in legal text summarization research published between 2019 and 2025, covering neural extractive methods, abstractive architectures, and hybrid frameworks that incorporate domain knowledge. Evaluation metrics such as ROUGE, BLEU, and BERTScore are discussed together with benchmark datasets developed across multiple jurisdictions. The study highlights key limitations, including the scarcity of annotated legal datasets, multilingual challenges, and concerns related to factual consistency in transformer-based models. It also discusses applications of summarization in improving accessibility of legal information and supporting judicial analysis workflows. The review concludes by emphasizing the importance of transparent, domain-adapted, and reliable summarization systems that achieve an effective balance between precision, interpretability, and efficiency.

Intelligent Edge-Cloud Architecture for Ultra-Low Latency Real-Time Remote Musical Collaboration

2026-05-30T12:28:08+00:00

This work explores the design and implementation of an intelligent edge-cloud architecture to enable ultra-low latency real-time remote musical collaboration. In distributed musical environments, maintaining precise timing and synchronization is critical, as even minor delays can negatively impact performance quality. Conventional cloud-centric systems often struggle to meet these strict latency requirements due to centralized processing, longer transmission paths, and network variability. To overcome these challenges, this study proposes a hybrid framework that combines the strengths of edge computing and cloud-based coordination. In the proposed architecture, edge nodes are strategically deployed near users to perform delay-sensitive operations such as audio capture, preprocessing, mixing, and temporary buffering. This localized processing significantly reduces round-trip communication time. The cloud layer complements this by managing global synchronization, coordinating multiple participants, and applying intelligent algorithms for latency prediction and compensation. Additionally, an adaptive network control mechanism dynamically optimizes routing paths and bandwidth allocation to maintain consistent performance under varying network conditions. The system was evaluated through a series of experiments involving multiple users, high-resolution audio streams, and mixed network environments, including 5G and fiber connections. Key performance indicators such as end-to-end latency, jitter, packet loss, and synchronization accuracy were measured and analyzed. The results indicate that the proposed solution achieves an average latency of approximately 15 milliseconds, which falls within the acceptable threshold for real-time musical interaction. Furthermore, improvements in synchronization stability and reduction in packet loss were observed compared to traditional approaches. Overall, this work demonstrates that integrating edge intelligence with cloud capabilities provides an effective and scalable solution for real-time collaborative applications, particularly in latency-sensitive domains such as remote music performance.

AI-Driven Intelligent Transportation IoT Platform for Congestion Reduction

2026-06-12T04:22:14+00:00

The continuous expansion of urban populations, coupled with the growing need for efficient mobility solutions, has significantly worsened traffic congestion in contemporary cities. Conventional traffic management approaches, largely based on fixed, non-adaptive control mechanisms, are increasingly unable to cope with the complexity and variability of modern urban traffic systems. In response to these limitations, this study introduces an intelligent transportation platform driven by Artificial Intelligence (AI) and the Internet of Things (IoT), aimed at mitigating congestion and enhancing traffic flow through real-time data processing and adaptive decision-making. The proposed framework leverages the combined capabilities of AI, IoT, and Big Data technologies to facilitate continuous monitoring, anticipate congestion patterns, and enable dynamic traffic regulation. The research methodology is grounded in a comprehensive documentary review of recent scientific literature (2020–2025), which supports the identification of key technological components, system architectures, and analytical models relevant to intelligent transportation systems. The findings indicate that integrating AI with IoT technologies substantially improves transportation system performance. This integration enables real-time observation of traffic conditions, early detection of congestion hotspots, and more efficient route optimization. Furthermore, the platform enhances adaptive traffic signal control and supports data-driven decision-making processes, resulting in smoother traffic flow, reduced delays, and improved overall system efficiency. This study contributes to the advancement of intelligent transportation systems by proposing a scalable and integrated framework aligned with smart city development goals. It underscores the transformative potential of AI–IoT platforms in reshaping urban mobility and provides a solid foundation for future research and practical implementation.

Implementation and Visualization of CPU Scheduling Algorithms

2026-06-12T08:07:55+00:00

This study focuses on the implementation and evaluation of two important CPU scheduling algorithms, namely First Come First Serve (FCFS) and Shortest Job First (SJF). Both algorithms are implemented in the C programming language, and their execution behavior and outputs are analyzed in detail using Python. FCFS is one of the simplest scheduling techniques, where processes are executed in the order of their arrival. Although easy to implement, FCFS suffers from the convoy effect, in which shorter processes experience longer waiting times due to the execution of larger tasks first. On the other hand, SJF schedules processes based on the shortest burst time, which helps reduce average waiting time and improves overall system performance. However, SJF may lead to starvation of longer processes if shorter jobs continue to arrive. The generated results and visualizations demonstrate the working principles, advantages, and limitations of both scheduling algorithms under different process conditions. This study provides a clear understanding of how CPU scheduling techniques influence system efficiency and process execution performance.