Jul 2026· International Conference on Smart Communications and Networking· pp. 1-6· 0 citations· 14 references
Abstract
As urban populations grow, smart cities increasingly depend on real-time environmental monitoring to enable sustainable development and efficient urban management. Conventional IoT systems often suffer from limited communication range, high power consumption, and unreliable data transmission. This paper presents a novel, scalable IoT architecture for smartcity monitoring that fully leverages ESP32 microcontrollers with integrated edge computing. The proposed design combines ESP-NOW, Wi-Fi, and MQTT protocols within a mesh-enabled framework, reducing latency and energy usage while enhancing resilience and coverage. Data is preprocessed at the edge before centralized aggregation on a Raspberry Pi backend, minimizing network overhead. Experimental results demonstrate that the architecture reliably communicates data, maintains precision, and exhibits strong resilience against node failures, including automatic gateway node replacement when disruptions occur. The design ensures robust fault tolerance and efficient operation, making it a practical and cost-effective solution for nextgeneration urban monitoring infrastructures.
Experimental results show that by using an optimal buffer size, the edge node reduces SPI bus usage for reading and writing data to the SD card from 38.0% to 3.1%, which significantly minimizes delays caused by frequent SD card access and also lowers the edge node’s energy consumption.
L. Campoverde, M. Tropea, Floriano De Rango· International Conference on...· 0 citations
This paper introduces ADIMS (Adaptive Distributed Infrastructure Monitoring System), a cloud-based telemetry platform that facilitates real-time monitoring and management of distributed field infrastructure across various organizational contexts. ADIMS solves the complex problem of integrating diverse devices by supporting multiple communication protocols, including TCP/IP, MODBUS, LoRaWAN, Sigfox, and LTE. This makes it easy to get data from third-party sensors and equipment. The system architecture has three main layers: a field acquisition layer that supports multi-protocol communication, a cloud-based processing and analytics engine, and a customizable dashboard framework that gives operational and managerial insights. We show that ADIMS could be used in smart city infrastructure by looking at similar systems and finding that they could use 15–30% less energy and save 25–35% on predictive maintenance costs. The platform’s protocol-agnostic design makes it easy to set up quickly at universities, NGOs, government buildings, and industrial sites. This helps achieve sustainable resource management and climate action goals that align with the UN Sustainable Development Goals.
Kevin Nyapom, David Padi· International Journal of Fut...· 0 citations
Abstract:
Smart cities leverage emerging technologies to enhance urban living, improve resource efficiency, and enable sustainable development. Among these technologies, the Internet of Things (IoT) plays a pivotal role by connecting physical devices, sensors, and systems to collect and exchange real-time data. This interconnected infrastructure enables intelligent monitoring and management of urban services such as transportation, energy distribution, waste management, water supply, and public safety. IoT-based smart city systems facilitate data-driven decision-making, reduce operational costs, and improve service delivery to citizens. By integrating cloud computing, edge computing, and advanced analytics, cities can respond dynamically to changing conditions and optimize resource utilization. Despite its advantages, challenges such as data security, interoperability, scalability, and privacy concerns must be addressed for successful implementation. Overall, IoT-enabled smart city infrastructure represents a transformative approach to building efficient, resilient, and citizen-centric urban environments.
Keywords: Smart City, Internet of Things (IoT), Urban Infrastructure, Sensor Networks, Real-time Data, Cloud Computing, Edge Computing, Smart Governance, Data Analytics, Sustainable Development.
G. Nagarjuna, Gudikandula Sai Krishna, K. Rakshitha· International Scientific Jou...· 0 citations
Low-cost Internet of Things (IoT) weather stations enhance spatial and temporal coverage for hyperlocal forecasting, especially in remote or hard-to-reach areas where traditional monitoring infrastructure is limited. However, their dependable operation is affected by component reliability, message delivery performance, and energy-related constraints, particularly battery depletion and solar recharge variability. This paper presents a dependability analysis of a real IoT-enabled weather monitoring platform based on a Weather Monitoring Approach (WMA), modeled using Stochastic Petri Nets (SPNs) to evaluate availability and reliability, while explicitly modeling energy autonomy as a cross-cutting operational constraint that affects continuous operation. Results show that the proposed WMA significantly increases operational availability, reduces failure probability, and improves energy autonomy by reducing the likelihood of battery depletion and extending operational continuity. In addition, the optimized communication configuration substantially decreased the latency required for near-certain message delivery, highlighting the impact of transmission tuning on system dependability. The proposed WMA provides a means to analyze configuration and design changes that can further improve system dependability, demonstrating how the combination of reliability modeling, energy autonomy mechanisms, and efficient communication strategies can substantially enhance the dependability of IoT-enabled weather monitoring systems and support continuous operation in regions with limited maintenance accessibility.
Vinícus Lima, B. Nogueira, Willy Tiengo et al.· Journal of Software and Syst...· 0 citations
Environmental monitoring facilitates solutions to major worldwide challenges, including air pollution, climate change, and water resource degradation. Yet, conventional cloud-based IoT systems are unable to provide real-time solutions because of issues like latency, increased energy consumption, and limited scalability. This paper aims to present a positive environmental impact of edge computing for real-time environmental monitoring and provide a sustainable, energy-efficient, low-latency environmental monitoring solution. The Edge Computing Real-Time Environmental Monitoring (ECRM) framework of the paper achieves local data processing and decision-making through the integration of edge intelligence, collective, and low-power machine learning models at edge gateways. The framework achieves system responsiveness and low energy consumption through the integration of energy-aware task scheduling and adaptive data transmission strategies. Processed data for air quality (AQI), CO₂, humidity, and temperature levels substantially reduce the framework's reliance on cloud computing. The framework provides a 39% reduction in energy consumption and a 40% reduction in latency in comparison to established cloud system models. The reductions improve real-time system responsiveness and reduce network traffic. The research demonstrates that combining edge architecture and green computing is a potential solution for sustainable environmental monitoring. The proposed systems align with the goals of computing sustainability and future smart city solutions.
Dawakit Lepcha, Kanchan Thakur· 2026 4th International Confe...· 0 citations
Rapid urbanization has increased the need for intelligent and sustainable smart city infrastructure management. Smart cities generate massive amounts of data through IoT devices, sensors, cloud platforms, and communication networks, making traditional centralized AI systems less effective due to scalability, latency, privacy, and reliability challenges. Distributed Artificial Intelligence (DAI) addresses these issues by distributing intelligence across multiple interconnected nodes, enabling decentralized learning and decision-making. This study examines the role of DAI technologies such as multi-agent systems, edge computing, federated learning, IoT networks, and cloud-edge collaboration in managing urban services. A review of recent applications demonstrates DAI’s effectiveness in traffic management, energy distribution, water systems, predictive maintenance, public safety, and environmental monitoring. The proposed framework enhances real-time processing, resource optimization, fault tolerance, and data privacy. Results indicate that DAI outperforms centralized approaches in response time, scalability, accuracy, and reliability. The study concludes that DAI is a key enabler of future smart cities, with emerging technologies such as Explainable AI (XAI), blockchain, digital twins, and autonomous urban management expected to further improve smart city operations and citizen services.
R. Sharma· International Journal of App...· 0 citations
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