Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 809-814· 0 citations· 19 references
Abstract
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.
In recent years, the concepts of sustainability and green computing have gained significant attention, particularly in the context of smart cities and their various transportation applications. The primary goal is to shift transportation from fuel-based systems to electric alternatives, reducing overall CO
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emissions. Motivated by this objective, this paper proposes a Green, Sustainable, and Energy-Efficient System for Transportation Applications in IoT Edge Cloud Networks. The focus is on designing an IoT edge cloud infrastructure to support sustainable and green transportation within smart cities. The system addresses various transportation-related tasks, including energy consumption monitoring, traffic and object detection, and optimal route planning, all while leveraging green edge cloud networks. To optimize performance, we propose a workload partitioning method based on a min-cut scheme that categorizes tasks into IoT-local, edge, and cloud-based workloads. This partitioning aims to reduce computational energy consumption and lower CO
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emissions, fostering a more eco-friendly environment. Additionally, we introduce the Energy-Efficient Application Partitioning and Task Scheduling (EAPTS) scheme, which efficiently divides and schedules tasks across different nodes. To validate the system, we implemented testbeds based on Oslo’s public transport scenario, used training data from the given dataset, and developed a simulator for a green, sustainable transport environment. Simulation results demonstrate that the proposed system effectively reduces CO
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emissions, energy consumption, and execution time for all operational tasks.
P. Khuwuthyakorn, A. Lakhan, Arnab Majumdar et al.· Scientific Reports· 0 citations
A Hybrid Digital Twin–IoT Framework that integrates real-time IoT sensing, cloud-edge computing, machine learning, and Digital Twin simulation for intelligent energy optimization for scalable, sustainable, and energy-efficient smart building management with improved reliability and decision-making is proposed.
Mahabala H. N.· International Journal of Mod...· 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 expansion of the Internet of Things (IoT) has led to an exponential increase in data volume, creating challenges for efficient data processing and latency control. Traditional cloud-based systems often experience excessive latency, making them less suitable for real-time applications. This research paper proposes a hardware-based, efficient task offloading framework using an IoT-Fog-Cloud architecture. An ESP8266-based IoT device senses real-time temperature data and transmits it via the MQTT protocol. The fog layer is implemented using Node-RED, which performs real-time data processing and decision-making, and generates alerts based on predefined temperature thresholds. This minimizes the dependency on the cloud for immediate responses. The processed data is then offloaded to the cloud layer using InfluxDB for data storage and Grafana for visualization and analysis. To measure task offloading performance, the proposed system includes a latency comparison between fog-layer processing time and cloud-layer response time. Experimental results demonstrate a significant reduction in response latency at the fog layer (avg. 103 ms) compared to cloud-layer response time (avg. 271 ms), representing approximately 62% lower latency.
Syed Faizan Haider· Journal of IoT-based Distrib...· 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
: Green Cloud Computing has become a sustainable computing paradigm which seeks to minimize energy usage, carbon dioxide emissions and the cost of operating systems while keeping performance peak in Cloud Computing systems with a high level of services. Clustering of all cloud computing, big data analytics, Artificial Intelligence (AI) is putting enormous pressure on the world's economy and the environment to supply the energy needed for its escalating growth. Efficient use of the computer resources such as job scheduling, load balancing and fault tolerance have become essential to maximize cloud operations and ensure sustainable utilization of computer resources. Task scheduling allows to schedule tasks to the right resources to minimize execution time and energy, load balancing allows to distribute the tasks equally between servers to avoid hot spots and optimize the use of resources. To ensure service availability and system reliability, fault-tolerance mechanisms offer fault detection, prediction and recovery in the event of hardware, software and network failure. To solve these challenges, the Deep Learning (DL) techniques have been found to be effective solutions for the intelligent workload prediction, adaptive resource allocation, proactive fault detection and automatic decision making. Cloud systems can be designed to be environmentally friendly while providing good service quality and performance by integrating carbon emission factors with scheduling, load balancing and fault-tolerant decision making. Combining DL models with the job scheduling, load balancing and fault-tolerance features of the cloud computing systems, cloud service providers can optimize even further energy consumption, lower carbon footprint, enhance Quality of Service (QoS) and improve service dependability. Optimization algorithms, Reinforcement Learning (RL), model-based methods, Neural Networks and hybrid intelligent approaches have been the recent developments with great promise for developing sustainable and resilient cloud computing environments. This article discusses several cloud-based research efforts and offers a thorough overview and comparison of current strategies for task scheduling that is both energy efficient and aware of carbon emissions. By systematically reviewing existing resource management techniques, this study highlights critical gaps and opportunities for improvement. Researchers interested in innovative cloud computing and discovering ways to lower emissions of carbon in cloud settings will find the results useful.
M. Dhanalakshmi, S. Manoharan· International Journal of Sci...· 0 citations
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