Jul 2026· International Journal of Drug Delivery Technology· Vol 16· 0 citations· 11 references
TL;DR
This paper examines how edge computing offloads tasks such as data pre-processing, anomaly detection, and local decision-making, thus minimizing the need for continuous data transmission to the cloud, thus minimizing the need for continuous data transmission to the cloud.
Abstract
Environmental monitoring is essential for tracking parameters such as air quality, temperature, humidity, and pollution
levels in real-time. However, the scale and energy demands of such IoT-based systems are often challenging. This
paper proposes a novel approach to enhance the energy efficiency of IoT-based environmental monitoring systems by
integrating edge computing. By processing data closer to the source (at the edge of the network), the system reduces
the energy consumption of communication and central processing, leading to significant energy savings. We examine
how edge computing offloads tasks such as data pre-processing, anomaly detection, and local decision-making, thus
minimizing the need for continuous data transmission to the cloud. The performance of this system is evaluated
through simulation experiments on various environmental monitoring scenarios, demonstrating that edge computing
integration significantly improves energy efficiency without compromising data accuracy or real-time monitoring
capabilities. The proposed approach is compared with traditional cloud-based IoT systems and the results highlight
the benefits of edge computing in energy-critical applications.
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
This study aimed to (1) design and develop an IoT-based hardware system for real-time air pollution monitoring, (2) develop an interactive mobile application for reporting and alert notifications, and (3) evaluate the system’s spatial stability and data transmission performance. The proposed framework integrates Internet of Things (IoT) technology with cloud-based database architecture. The sensing node incorporates a SEN55 environmental sensor and an ESP32 microcontroller to collect key parameters, including PM2.5, VOC Index, and NOx Index. Sensor readings are processed using an edge aggregation algorithm to reduce signal noise before being stored in Firebase Realtime Database for real-time synchronization with the “React AQI” mobile application, developed using the React Native framework. The application supports multilingual visualization in Thai, English, and Chinese. Experimental results demonstrated a high average Data Delivery Ratio (DDR) of 94.76%, with data reliability ranging between 95.7% and 97.5%. A 20-day spatial comparative deployment conducted at Building 22 and Building 26 revealed consistent differentiation in pollution levels across locations, with a mean PM2.5 difference of 1.54 µg/m3. The system effectively visualized pollution trends through interactive graphical analytics. These findings indicate that the proposed framework serves as an innovative prototype for Smart Health management within higher education institutions, aligned with the global Smart Campus paradigm.
S. Janpla, Thanakorn Uiphanit· International Journal of Int...· 0 citations
Environmental pollution has become a major global concern due to rapid industrialization, urbanization, and increasing vehicular emissions. Continuous monitoring of air quality and environmental parameters is essential for protecting public health and maintaining ecological balance. This paper presents an Internet of Things (IoT)-based Environmental Pollution Monitoring System designed to collect, analyze, and transmit real-time environmental data. The proposed system utilizes various sensors to measure parameters such as air quality, temperature, humidity, and harmful gas concentrations. Sensor data are processed by a microcontroller and transmitted to a cloud platform through wireless communication technologies, enabling remote monitoring and analysis. The system provides timely alerts when pollution levels exceed predefined thresholds, facilitating prompt corrective actions by authorities and stakeholders. The implementation of IoT technology enhances the accuracy, scalability, and accessibility of environmental monitoring while reducing operational costs. Experimental results demonstrate the effectiveness of the proposed system in providing real-time pollution assessment and supporting sustainable environmental management.
Keywords— Internet of Things (IoT), Environmental Pollution Monitoring, Air Quality Monitoring, Smart Sensors, Wireless Sensor Networks, Cloud Computing, Real-Time Monitoring, Environmental Sustainability, Smart Environment, Data Analytics.
Ajmeera Anil Ajmeera Anil, Lokaboina Vaishali Lokaboina Vaishali, Prof A K Rahtod Prof A K Rahtod· International Journal of Cre...· 0 citations
This work presents a multi-mode energy harvesting-assisted edge computing architecture, integrated with a joint optimization of energy consumption and communication behaviour, aimed at enhancing the sustainability, reliability and autonomy of operation in an industrial IoT context.
Dr. Deepa, M. Mehfooza, Padmavathy Thiruppathi Raj· Microsystem Technologies· 0 citations
The rapid growth in electricity consumption has increased the need for intelligent energy management and protection systems. Conventional electrical monitoring systems lack real-time analysis and remote accessibility, leading to energy wastage and delayed fault response. This paper presents a Smart Energy Optimization and Protection System using Internet of Things (IoT) technology for efficient monitoring, fault detection, and energy management. The proposed system continuously monitors electrical parameters such as voltage, current, and power consumption using sensors interfaced with an embedded controller. The collected data is transmitted to a cloud platform through IoT connectivity, enabling real-time monitoring and remote access through mobile or web applications. The system identifies abnormal conditions such as overload, short circuit, over-voltage, and excessive energy consumption using threshold-based analysis. Upon detecting faults, immediate alerts and protection mechanisms are activated to prevent equipment damage and improve system safety. Additionally, the system provides energy optimization suggestions to reduce unnecessary power consumption. The integration of IoT enhances system scalability, accessibility, and efficiency, making the proposed model suitable for residential, commercial, and industrial applications.,
K. Thamizhazhakan, D. Vinoth, S. Bharanivelan et al.· 2026 6th International Confe...· 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, Bruno Nogueira, Willy Tiengo et al.· Journal of Software and Syst...· 0 citations