Current Issue : October-December Volume : 2025 Issue Number : 4 Articles : 5 Articles
Sensor Networks (SNs) are gaining more attention in applications such as urban microclimate monitoring, which is a critical input for building energy simulation. Despite extensive research on SN placement, there remains a shortage of studies on efficient solutions that account for realistic sensing models without oversimplifying the environment or search spaces. As a result, existing methods often fall short when applied to large-scale, real-world problems. This study proposes a realistic coverage model for point-based sensor networks (e.g., air temperature sensors) and introduces a novel and efficient heuristic Voronoi-based Optimal Sensor Deployment Algorithm (VOSDA). The algorithm estimates the minimum number of sensors needed and their optimal placement.VOSDAleveragesVoronoi diagram characteristics to manage the sensor network, assess error distribution, and enhance coverage quality through integrated sensor insertion and movement strategies. Its performance is evaluated using the root mean square error (RMSE), calculated via an interpolation process that reconstructs the field from sensor positions. Several experiments were conducted to evaluate the effectiveness and efficiency of the proposed approach, comparing the results with the Genetic Algorithm (GA) as a reference, by calculating the RMSE using Kriging, Thin Plate Spline, and Inverse Distance Weighting methods. In all cases, VOSDA was first used to estimate the required number of sensors, and RMSE was then calculated for both algorithms at that sensor count. Furthermore, in six out of nine different scenarios conducted across different benchmark heatmaps, VOSDA outperformed GA in achieving lower RMSE values. Both algorithms performed significantly better with Kriging and TPS than with IDW....
Underwater acoustic sensor networks, crucial for marine monitoring, face significant challenges, including limited bandwidth, high delay, and severe energy constraints. Addressing these limitations requires an energy-efficient design to ensure network survivability, reliability, and reduced operational costs. This paper proposes an artificial intelligence-driven framework aimed at enhancing energy efficiency and sustainability in applications of marine wildlife monitoring in underwater sensor networks, according to the vision of implementing an underwater acoustic sensor network. The framework integrates intelligent computing directly into underwater sensor nodes, employing lightweight AI models to locally classify marine species. Transmitting only classification results, instead of raw data, significantly reduces data volume, thus conserving energy. Additionally, a software-defined radio methodology dynamically adapts transmission parameters such as modulation schemes, packet length, and transmission power to further minimize energy consumption and environmental disruption. GNU Radio simulations evaluate the framework effectiveness using metrics like energy consumption, bit error rate, throughput, and delay. Adaptive transmission strategies implicitly ensure reduced energy usage as compared to non-adaptive transmission solutions employing fixed communication parameters. The results illustrate the framework ability to effectively balance energy efficiency, performance, and ecological impact. This research contributes directly to ongoing development in sustainable and energy-efficient underwater wireless sensor network design and deployment....
Quantum computing emerges as a revolutionary force in distributed systems optimization, fundamentally transforming resource allocation and system management paradigms. The integration of quantum algorithms with classical infrastructure introduces unprecedented capabilities in addressing complex optimization challenges in microservice architectures. Through quantumenhanced protocols and hybrid quantum-classical systems, distributed computing achieves remarkable improvements in efficiency, scalability, and performance. The combination of Quantum Approximate Optimization Algorithm (QAOA) and Variational Quantum Eigensolver (VQE) with traditional computing frameworks enables superior resource management, enhanced decision-making capabilities, and optimized service mesh configurations. This convergence of quantum and classical computing paradigms paves the way for next-generation distributed systems that can handle increasingly complex optimization challenges while maintaining operational efficiency....
Real-time distributed systems are fundamentally transforming urban transportation networks, creating smarter, more responsive infrastructure capable of addressing complex mobility challenges. This article examines how distributed computing architectures enable immediate analysis of traffic conditions, facilitate autonomous vehicle coordination, and enhance emergency response capabilities across transportation ecosystems. The technical foundations supporting these advancements include edge computing deployments, sensor networks, and communication protocols that collectively enable intelligent traffic management. The integration of these technologies into public transit systems, traffic signal controls, and safety applications demonstrates significant improvements in urban mobility efficiency. Through demonstration of implementation challenges such as connectivity reliability and latency requirements, alongside case studies from cities pioneering smart transportation initiatives, this article provides a comprehensive framework for understanding how real-time distributed systems revolutionizing transportation management are while promoting environmental sustainability and public safety....
This paper addresses the critical challenge of time management in wireless sensor networks (WSNs) applied to industrial process control. Although wireless technologies have gained ground in industrial monitoring, their adoption in control applications remains limited due to concerns around reliability and timing accuracy. This study proposes a practical, low-cost solution based on commercial off-the-shelf (COTS) components, leveraging the IEEE 802.15.4-2020 standard in Time-Slotted Channel-Hopping (TSCH) mode. A custom time management algorithm is developed and implemented on STM32 microcontrollers paired with AT86RF212B transceivers. The proposed system ensures a sub-millisecond synchronization drift across nodes by dividing communication into a structured slot frame and implementing precise scheduling and enhanced beacon-based synchronization. Validation is performed through experimental setups monitored with logic analyzers, demonstrating a time drift consistently below 600 microseconds. The results confirm the feasibility of using synchronized wireless nodes for real-time industrial control tasks, suggesting that further improvements in hardware precision could enable even tighter synchronization and broader applicability in fast and critical processes....
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