Pub Date : 2025-09-15DOI: 10.1016/j.suscom.2025.101210
G. Balram , KDV Prasad , Kamalakar Ramineni , Rahul Divgan , K. Ashok , N.V. Phani Sai Kumar
Intelligent transportation systems require routing protocols that optimize both performance and environmental impact simultaneously in 5G-enabled Vehicular Ad Hoc Networks (VANETs). Existing solutions often treat sustainability as a secondary constraint, which limits their effectiveness in addressing climate change goals. This study presents MEDALS (Metaheuristic-Enhanced Deep Adaptive Learning System), a hybrid framework that integrates deep reinforcement learning with metaheuristic optimization to achieve both superior performance and environmental sustainability. The system introduces the Green Performance Index (GPI), the first comprehensive metric combining energy efficiency, carbon footprint, latency, and reliability. Through extensive evaluation using industry-standard simulators, MEDALS demonstrates statistically significant improvements: MEDALS achieves 96.8 % energy efficiency (+11.6 %), 0.73 ms latency (-91.6 %), 99.7 % reliability, and 42.3 % carbon reduction while scaling to 1000 + vehicles with linear computational complexity. This will allow its practical implementation in smart cities and towards fulfillment of the sustainable development goals. This complexity augmentation of 3.3x times in the network size handling is attributed to the hybrid intelligence architecture of the framework, the adaptive deep reinforcement learning with the dual metaheuristic optimisation in intelligent fusion mechanism, and the empirically quantified O(N log N) complexity.
{"title":"MEDALS: A sustainable AI framework for energy-efficient routing in 5G vehicular networks","authors":"G. Balram , KDV Prasad , Kamalakar Ramineni , Rahul Divgan , K. Ashok , N.V. Phani Sai Kumar","doi":"10.1016/j.suscom.2025.101210","DOIUrl":"10.1016/j.suscom.2025.101210","url":null,"abstract":"<div><div>Intelligent transportation systems require routing protocols that optimize both performance and environmental impact simultaneously in 5G-enabled Vehicular Ad Hoc Networks (VANETs). Existing solutions often treat sustainability as a secondary constraint, which limits their effectiveness in addressing climate change goals. This study presents MEDALS (Metaheuristic-Enhanced Deep Adaptive Learning System), a hybrid framework that integrates deep reinforcement learning with metaheuristic optimization to achieve both superior performance and environmental sustainability. The system introduces the Green Performance Index (GPI), the first comprehensive metric combining energy efficiency, carbon footprint, latency, and reliability. Through extensive evaluation using industry-standard simulators, MEDALS demonstrates statistically significant improvements: MEDALS achieves 96.8 % energy efficiency (+11.6 %), 0.73 ms latency (-91.6 %), 99.7 % reliability, and 42.3 % carbon reduction while scaling to 1000 + vehicles with linear computational complexity. This will allow its practical implementation in smart cities and towards fulfillment of the sustainable development goals. This complexity augmentation of 3.3x times in the network size handling is attributed to the hybrid intelligence architecture of the framework, the adaptive deep reinforcement learning with the dual metaheuristic optimisation in intelligent fusion mechanism, and the empirically quantified O(N log N) complexity.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101210"},"PeriodicalIF":5.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-15DOI: 10.1016/j.suscom.2025.101208
Jyotsnarani Tripathy , M. Kaliappan , Gnana Kousalya Chellathevar , J. Relin Francis Raj , Ravivarman Shanmugasundaram , Manjunathan Alagarsamy , S.Patricia Nancy , Ali Algahtani
In a world that is rapidly urbanising and EV-dependent, the energy efficiency and sustainability of transportation infrastructures is a daunting challenge. It introduces the Blockchain-Based IoT Urban Transport Optimizer (BIUTO), a new approach that combines IOT, blockchain and predictive modeling for traffic management, EV charging efficiency, and building energy consumption. The framework aims to circumvent major drawbacks of current centralized architectures, such as data breaches, scaling, and inability to dynamically manage nested urban systems. The technology leverages IoT for real-time collection, Machine Learning Models (LSTM, Gradient Boosted Decision Trees, or GBDT) for predictive analytics, and blockchain for secure and decentralized data storage. The traffic subsystem helped reduce peak congestion by 23 % via real-time traffic flow prediction, and the EV charging subsystem increased energy efficiency by 15 %. The building energy efficiency subsystem reported high RMSE values for heating and cooling loads. The blockchain layer made data secure and transparent, alleviating issues with centralized system malfunctions. This work brings to the table a single, scaling approach to sustainable city transport based on energy efficiency, which will also serve as part of the sustainability agenda worldwide. The flexibility of BIUTO to integrate multiple urban subsystems represents an enormous step forward towards smart, low-carbon cities. Future efforts will include scaling the blockchain latency and scaling up the model to include renewable energy.
{"title":"Integrating blockchain and iot with advanced predictive modeling for energy efficient urban transportation systems","authors":"Jyotsnarani Tripathy , M. Kaliappan , Gnana Kousalya Chellathevar , J. Relin Francis Raj , Ravivarman Shanmugasundaram , Manjunathan Alagarsamy , S.Patricia Nancy , Ali Algahtani","doi":"10.1016/j.suscom.2025.101208","DOIUrl":"10.1016/j.suscom.2025.101208","url":null,"abstract":"<div><div>In a world that is rapidly urbanising and EV-dependent, the energy efficiency and sustainability of transportation infrastructures is a daunting challenge. It introduces the Blockchain-Based IoT Urban Transport Optimizer (BIUTO), a new approach that combines IOT, blockchain and predictive modeling for traffic management, EV charging efficiency, and building energy consumption. The framework aims to circumvent major drawbacks of current centralized architectures, such as data breaches, scaling, and inability to dynamically manage nested urban systems. The technology leverages IoT for real-time collection, Machine Learning Models (LSTM, Gradient Boosted Decision Trees, or GBDT) for predictive analytics, and blockchain for secure and decentralized data storage. The traffic subsystem helped reduce peak congestion by 23 % via real-time traffic flow prediction, and the EV charging subsystem increased energy efficiency by 15 %. The building energy efficiency subsystem reported high RMSE values for heating and cooling loads. The blockchain layer made data secure and transparent, alleviating issues with centralized system malfunctions. This work brings to the table a single, scaling approach to sustainable city transport based on energy efficiency, which will also serve as part of the sustainability agenda worldwide. The flexibility of BIUTO to integrate multiple urban subsystems represents an enormous step forward towards smart, low-carbon cities. Future efforts will include scaling the blockchain latency and scaling up the model to include renewable energy.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101208"},"PeriodicalIF":5.7,"publicationDate":"2025-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-13DOI: 10.1016/j.suscom.2025.101200
Guilin He, Min Lei, Lei Han, Peifa Shan, Ruipeng Chen
Adopting the dynamic digital twin (DDT) model in smart grid distribution networks is a revolutionary breakthrough toward advanced dynamic energy management and control. However, even the most advanced systems fail to describe static architectural configuration adequately or they do not offer sufficient automation in this process, they are unable to handle dynamic interactions or topological hierarchy. To overcome such restrictions, this research presents a new framework for building DDT models based on Graph Neural Networks (GNNs). GNNs outperform other deep learning models when it comes to modeling graph-structured data which has application in modeling nodes and edges of smart grids. The adopted model expands the critical technical parameters' achievements and indicates a high Voltage Regulation Efficiency of 92 % and Network Efficiency belonging to 95 %; therefore, the distribution of power and operation reliability is considered optimal. The advantage of these findings is also echoed by the Voltage Profile Deviation of 0.015 p.u. and the Power Loss Reduction of 18.3 % which suggest that the proposed method offers better voltage profile stability and less energy losses than existing static models. The usefulness and applicability of the framework can be shown by performing experiments in MATLAB Simulink and Python-based libraries such as PyTorch Geometric. This study provides a novel approach to address issues in applied research and provides the basis for further advancements in realistic digital twin applications concerning smart grids.
{"title":"Energy-efficient smart grid operations through dynamic digital twin models and deep learning","authors":"Guilin He, Min Lei, Lei Han, Peifa Shan, Ruipeng Chen","doi":"10.1016/j.suscom.2025.101200","DOIUrl":"10.1016/j.suscom.2025.101200","url":null,"abstract":"<div><div>Adopting the dynamic digital twin (DDT) model in smart grid distribution networks is a revolutionary breakthrough toward advanced dynamic energy management and control. However, even the most advanced systems fail to describe static architectural configuration adequately or they do not offer sufficient automation in this process, they are unable to handle dynamic interactions or topological hierarchy. To overcome such restrictions, this research presents a new framework for building DDT models based on Graph Neural Networks (GNNs). GNNs outperform other deep learning models when it comes to modeling graph-structured data which has application in modeling nodes and edges of smart grids. The adopted model expands the critical technical parameters' achievements and indicates a high Voltage Regulation Efficiency of 92 % and Network Efficiency belonging to 95 %; therefore, the distribution of power and operation reliability is considered optimal. The advantage of these findings is also echoed by the Voltage Profile Deviation of 0.015 p.u. and the Power Loss Reduction of 18.3 % which suggest that the proposed method offers better voltage profile stability and less energy losses than existing static models. The usefulness and applicability of the framework can be shown by performing experiments in MATLAB Simulink and Python-based libraries such as PyTorch Geometric. This study provides a novel approach to address issues in applied research and provides the basis for further advancements in realistic digital twin applications concerning smart grids.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101200"},"PeriodicalIF":5.7,"publicationDate":"2025-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096416","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-12DOI: 10.1016/j.suscom.2025.101202
Salim El khediri , Pascal Lorenz
Cluster-based routing has been effective for facing the unique problems of Wireless Sensor Networks such as handling energy consumption and forwarding data in large, limited resource environments. Based on how the Aurora Borealis changes over time, this paper proposes the Aurora-Based Clustering Protocol which relies on virtual electrical drift and quantum tunneling to select flexible clusters and their heads. According to ABCP, a sensor node is represented by a charged particle and its virtual charge is measured by considering remaining energy and nearby data amounts. Nodes in the network are linked by streamlines created with magnetic-inspired methods and cluster heads are selected randomly using a fitness model that aims for both balance and central locations. It offers support for changing network arrangements and arranges paths so that communication is efficient wherever and whenever users move. ABCP was tested by running multiple simulations with a network of 300 nodes which reflects how a WSN might be used in real life. Against standard approaches such as LEACH, BeeCluster, iABC and PSO-based schemes, ABCP saves up to 28.7% more energy and adds at least 17.4% to the network’s lifetime under varying and densely packed node conditions.
{"title":"Energy-efficient communication in WSNs using ABCP: An Aurora and quantum tunneling approach","authors":"Salim El khediri , Pascal Lorenz","doi":"10.1016/j.suscom.2025.101202","DOIUrl":"10.1016/j.suscom.2025.101202","url":null,"abstract":"<div><div>Cluster-based routing has been effective for facing the unique problems of Wireless Sensor Networks such as handling energy consumption and forwarding data in large, limited resource environments. Based on how the Aurora Borealis changes over time, this paper proposes the Aurora-Based Clustering Protocol which relies on virtual electrical drift and quantum tunneling to select flexible clusters and their heads. According to ABCP, a sensor node is represented by a charged particle and its virtual charge is measured by considering remaining energy and nearby data amounts. Nodes in the network are linked by streamlines created with magnetic-inspired methods and cluster heads are selected randomly using a fitness model that aims for both balance and central locations. It offers support for changing network arrangements and arranges paths so that communication is efficient wherever and whenever users move. ABCP was tested by running multiple simulations with a network of 300 nodes which reflects how a WSN might be used in real life. Against standard approaches such as LEACH, BeeCluster, iABC and PSO-based schemes, ABCP saves up to 28.7% more energy and adds at least 17.4% to the network’s lifetime under varying and densely packed node conditions.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101202"},"PeriodicalIF":5.7,"publicationDate":"2025-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096418","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-10DOI: 10.1016/j.suscom.2025.101198
Deepshikha Shrivastava , Prerna Goswami
Monitoring energy consumption in buildings presents significant opportunities, especially in developing economies like India. However, current solutions often overlook cost-effective, small-scale, accurate, and open-source data-driven methodologies. Research in this area is often hindered by concerns related to security and privacy, high investment costs, and unpredictable returns. To address these challenges, we developed an automated hybrid deep learning and Internet of Things (DL-IoT) building energy management system (BEMS) aimed at conserving energy. The DL-IoT combines deep learning techniques with fuzzy logic to effectively manage uncertainty and noise in electrical properties. Our DL-IoT regression model demonstrated low mean absolute error and mean squared error, achieving a coefficient of determination of 0.99 for out-of-sample energy consumption predictions. We extracted twenty-seven electricity usage variables from raw data to train the model. Experimental results revealed a linear relationship between these characteristics and energy use. The proposed model successfully predicted features that could contribute to energy savings, such as Power Factor and Power in the Y Phase. Specifically, it estimated that a one-unit increase in Power in the Y Phase and Power Factor would result in a reduction in energy consumption. The findings of the experiment indicated that the model captured the variability of the data better than other models. The results demonstrated the superiority of the proposed model over other mainstream existing models. Through the results of this paper, a more efficient energy data management and consumption plan can be established.
{"title":"Automated deep learning and Internet of Things framework for building energy management: A university case study","authors":"Deepshikha Shrivastava , Prerna Goswami","doi":"10.1016/j.suscom.2025.101198","DOIUrl":"10.1016/j.suscom.2025.101198","url":null,"abstract":"<div><div>Monitoring energy consumption in buildings presents significant opportunities, especially in developing economies like India. However, current solutions often overlook cost-effective, small-scale, accurate, and open-source data-driven methodologies. Research in this area is often hindered by concerns related to security and privacy, high investment costs, and unpredictable returns. To address these challenges, we developed an automated hybrid deep learning and Internet of Things (DL-IoT) building energy management system (BEMS) aimed at conserving energy. The DL-IoT combines deep learning techniques with fuzzy logic to effectively manage uncertainty and noise in electrical properties. Our DL-IoT regression model demonstrated low mean absolute error and mean squared error, achieving a coefficient of determination of 0.99 for out-of-sample energy consumption predictions. We extracted twenty-seven electricity usage variables from raw data to train the model. Experimental results revealed a linear relationship between these characteristics and energy use. The proposed model successfully predicted features that could contribute to energy savings, such as Power Factor and Power in the Y Phase. Specifically, it estimated that a one-unit increase in Power in the Y Phase and Power Factor would result in a reduction in energy consumption. The findings of the experiment indicated that the model captured the variability of the data better than other models. The results demonstrated the superiority of the proposed model over other mainstream existing models. Through the results of this paper, a more efficient energy data management and consumption plan can be established.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101198"},"PeriodicalIF":5.7,"publicationDate":"2025-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145109549","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-09DOI: 10.1016/j.suscom.2025.101204
Muhammad Zohaib , Seyed-Sajad Ahmadpour , Hadi Rasmi , Angshuman Khan , Nima Jafari Navimipour
Digital sustainable system plays a vital role in the advancement of dynamic industries, including agriculture, healthcare, smart cities, Edge Artificial Intelligence (AI), and the Internet of Things (IoT), by facilitating high-speed, low-power, and highly compressed processing. These systems are based on the capabilities of real-time execution, processing, and analysis of large-scale information with extreme power and area limitations. However, traditional Arithmetic Logic Units (ALUs) based on complementary metal-oxide semiconductors (CMOS) are becoming challenging in terms of scalability, power consumption, space demand, and nanoscale fabrication. The ALU is one of the most important parts of such systems and has a direct effect on the overall computing performance, but current implementations cannot sustain the requirements of next-generation applications. To overcome these shortcomings, this paper offers an area-efficient and low-latency ALU that can be designed with the quantum-dot cellular automata (QCA) technology, with the advantage of employing area-efficient layout and simple cell design. The proposed QCA-based ALU has high performance, less delay, and less energy consumption, which makes it properly suitable for the next generation of digital sustainable systems applications. The outcome of the simulation indicates that there are considerable performance gains, such as an 82.37% decrease in energy consumption, and a 9.21% decrease in area relative to current available design. These enhancements emphasize the power of QCA technology as a scalable and low-energy consumption alternative to CMOS in the realization of critical computing components in sustainable digital systems.
{"title":"A low-latency and area-efficient QCA-based quantum-dot design for next-generation digital sustainable systems","authors":"Muhammad Zohaib , Seyed-Sajad Ahmadpour , Hadi Rasmi , Angshuman Khan , Nima Jafari Navimipour","doi":"10.1016/j.suscom.2025.101204","DOIUrl":"10.1016/j.suscom.2025.101204","url":null,"abstract":"<div><div>Digital sustainable system plays a vital role in the advancement of dynamic industries, including agriculture, healthcare, smart cities, Edge Artificial Intelligence (AI), and the Internet of Things (IoT), by facilitating high-speed, low-power, and highly compressed processing. These systems are based on the capabilities of real-time execution, processing, and analysis of large-scale information with extreme power and area limitations. However, traditional Arithmetic Logic Units (ALUs) based on complementary metal-oxide semiconductors (CMOS) are becoming challenging in terms of scalability, power consumption, space demand, and nanoscale fabrication. The ALU is one of the most important parts of such systems and has a direct effect on the overall computing performance, but current implementations cannot sustain the requirements of next-generation applications. To overcome these shortcomings, this paper offers an area-efficient and low-latency ALU that can be designed with the quantum-dot cellular automata (QCA) technology, with the advantage of employing area-efficient layout and simple cell design. The proposed QCA-based ALU has high performance, less delay, and less energy consumption, which makes it properly suitable for the next generation of digital sustainable systems applications. The outcome of the simulation indicates that there are considerable performance gains, such as an 82.37% decrease in energy consumption, and a 9.21% decrease in area relative to current available design. These enhancements emphasize the power of QCA technology as a scalable and low-energy consumption alternative to CMOS in the realization of critical computing components in sustainable digital systems.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101204"},"PeriodicalIF":5.7,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096419","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-09DOI: 10.1016/j.suscom.2025.101205
Songzhi Zhang, Peng Sun
The current study optimizes a regional integrated energy system that combines concentrated solar power, wind turbines, energy storage, and thermal components to enhance energy efficiency, reduce costs, and minimize environmental impact. The primary objectives were to reduce operational expenses, address environmental concerns, and ensure a reliable electricity supply through integrated load response mechanisms. Fuzzy probability-constrained programming was used to model the uncertainty of renewable energy output, and a modified gravitational search algorithm (MGSA) was employed for optimization. Two different approaches to energy demand response were studied: one using electric boilers with a fixed thermoelectric power ratio, and another employing a flexible system for cooling, heating, and power that could adjust as needed. The implementation of the load response program resulted in a 0.75 % increase in the electrical peak-valley difference and a 0.51 % increase in the thermal peak-valley difference, indicating slight shifts in demand distribution. Additionally, valley values decreased by 0.37 % for electrical loads and by 2.71 % for thermal loads, suggesting modest improvements in off-peak load utilization. These changes demonstrate the program's potential to reshape load profiles; however, significant peak reduction will require further enhancement.
{"title":"Optimizing regional energy systems with concentrated solar power for enhanced efficiency, sustainability, and cost-effective energy management","authors":"Songzhi Zhang, Peng Sun","doi":"10.1016/j.suscom.2025.101205","DOIUrl":"10.1016/j.suscom.2025.101205","url":null,"abstract":"<div><div>The current study optimizes a regional integrated energy system that combines concentrated solar power, wind turbines, energy storage, and thermal components to enhance energy efficiency, reduce costs, and minimize environmental impact. The primary objectives were to reduce operational expenses, address environmental concerns, and ensure a reliable electricity supply through integrated load response mechanisms. Fuzzy probability-constrained programming was used to model the uncertainty of renewable energy output, and a modified gravitational search algorithm (MGSA) was employed for optimization. Two different approaches to energy demand response were studied: one using electric boilers with a fixed thermoelectric power ratio, and another employing a flexible system for cooling, heating, and power that could adjust as needed. The implementation of the load response program resulted in a 0.75 % increase in the electrical peak-valley difference and a 0.51 % increase in the thermal peak-valley difference, indicating slight shifts in demand distribution. Additionally, valley values decreased by 0.37 % for electrical loads and by 2.71 % for thermal loads, suggesting modest improvements in off-peak load utilization. These changes demonstrate the program's potential to reshape load profiles; however, significant peak reduction will require further enhancement.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101205"},"PeriodicalIF":5.7,"publicationDate":"2025-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118049","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-08DOI: 10.1016/j.suscom.2025.101206
Özlem Sabuncu , Bülent Bilgehan
Energy efficiency in Unmanned Aerial Vehicles (UAVs) is crucial for operations, where effective payload delivery, stabilization, and communication are essential. This study presents a nonlinear energy consumption model tailored for UAVs, built upon exponential scaling and multiplicative calculus to reflect the interdependencies among payload weight, wind speed, altitude, velocity and communication power. Unlike conventional approaches that rely on linear or polynomial formulations, the proposed method incorporates energy demands from integrated systems, focusing on energy consumption. The proposed multiplicative model provides valuable insights into the energy trade-offs influenced by changing environmental and operational conditions. It improves the practicality of using UAVs for real-time aid delivery, resource allocation, and communication in challenging, resource-constrained environments, offering better accuracy than traditional energy consumption models. Validation using experimental datasets demonstrates that the proposed model achieves an 85 % improvement in accuracy compared to the recently established cubic polynomial model for predicting energy consumption. The effectiveness of the proposed multiplicative model was evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) as performance metrics. The basic polynomial model recorded an MSE of 57.4269, while the parametric polynomial model significantly improved this to 5.7794. In comparison, the multiplicative model demonstrated superior accuracy, achieving a markedly lower MSE of 0.8472. Consistently, the multiplicative model also outperformed the others in terms of RMSE, attaining the lowest value of 0.9205, thereby confirming its robustness and predictive reliability. The Mean Absolute Error (MAE) was reduced from 6.44 to 0.73, representing an 88.66 % improvement. Furthermore, the R² value increased from 0.95 to 0.99, indicating a stronger fit between the predicted and actual data. These results underscore the multiplicative model's robustness, accuracy, and reliability, demonstrating its strong potential for real-world predictive applications. The findings demonstrate that the proposed model more accurately represents energy consumption, providing a robust foundation for precise analysis and design.
{"title":"Nonlinear energy modeling for UAVs in critical missions using multiplicative calculus","authors":"Özlem Sabuncu , Bülent Bilgehan","doi":"10.1016/j.suscom.2025.101206","DOIUrl":"10.1016/j.suscom.2025.101206","url":null,"abstract":"<div><div>Energy efficiency in Unmanned Aerial Vehicles (UAVs) is crucial for operations, where effective payload delivery, stabilization, and communication are essential. This study presents a nonlinear energy consumption model tailored for UAVs, built upon exponential scaling and multiplicative calculus to reflect the interdependencies among payload weight, wind speed, altitude, velocity and communication power. Unlike conventional approaches that rely on linear or polynomial formulations, the proposed method incorporates energy demands from integrated systems, focusing on energy consumption. The proposed multiplicative model provides valuable insights into the energy trade-offs influenced by changing environmental and operational conditions. It improves the practicality of using UAVs for real-time aid delivery, resource allocation, and communication in challenging, resource-constrained environments, offering better accuracy than traditional energy consumption models. Validation using experimental datasets demonstrates that the proposed model achieves an 85 % improvement in accuracy compared to the recently established cubic polynomial model for predicting energy consumption. The effectiveness of the proposed multiplicative model was evaluated using Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) as performance metrics. The basic polynomial model recorded an MSE of 57.4269, while the parametric polynomial model significantly improved this to 5.7794. In comparison, the multiplicative model demonstrated superior accuracy, achieving a markedly lower MSE of 0.8472. Consistently, the multiplicative model also outperformed the others in terms of RMSE, attaining the lowest value of 0.9205, thereby confirming its robustness and predictive reliability. The Mean Absolute Error (MAE) was reduced from 6.44 to 0.73, representing an 88.66 % improvement. Furthermore, the R² value increased from 0.95 to 0.99, indicating a stronger fit between the predicted and actual data. These results underscore the multiplicative model's robustness, accuracy, and reliability, demonstrating its strong potential for real-world predictive applications. The findings demonstrate that the proposed model more accurately represents energy consumption, providing a robust foundation for precise analysis and design.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101206"},"PeriodicalIF":5.7,"publicationDate":"2025-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060496","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-04DOI: 10.1016/j.suscom.2025.101203
D. Ajitha , Muhammad Zohaib , Firdous Ahmad , Khalid Zaman , S.M. Prabin
The Internet of Things (IoT) plays a vital role in the recent healthcare industry by providing precise diagnostic and treatment capabilities. There is a growing interest in medical IoT incorporated into healthcare systems. The processing unit of all medical IoT comprises complementary metal-oxide semiconductor (CMOS) technology. However, CMOS Medical IoT technology has become integrated into biomedical hardware systems at the nanoscale regime. Due to regulatory, ethical, and technological challenges, including slow processing speeds, high power consumption, and slow switching frequencies, particularly in the gigahertz (GHz) range. On the other hand, compared to traditional computers, quantum technology will accelerate processing by an order of magnitude and affect all artificial and medical (AI) and medical IoT processing applications. Quantum-dot cellular automata (QCA) present a promising alternative digital hardware system in medical IoT. QCA technology makes an optimal choice for establishing circuit design frameworks for AI in medical IoT applications, where low-cost, real-time, energy-efficient performance is crucial. Moreever, encryption and decryption circuits have been used in medical IoT operations to protect sensitive patient data while it is being transmitted and stored. The essential arithmetic and logic unit (ALU) is proposed in this context, which is the foundation for processing and computational units for medical IoT systems at the nanoscale devices. A systematic approach is involved in integrating adders, multiplexers, an ALU, and a logic unit to enhance processor drive and privacy via encryption and decryption in medical IoT. The experimental outcomes reveal that the recommended design overtakes the previous design by 15.48 % in terms of cells and 16.07 % in terms of area. The designs are accurately simulated using the QCADesigner-E 2.0.3 software tool.
{"title":"Efficient QCA‐Based Circuits for Low‐Power Medical IoT System","authors":"D. Ajitha , Muhammad Zohaib , Firdous Ahmad , Khalid Zaman , S.M. Prabin","doi":"10.1016/j.suscom.2025.101203","DOIUrl":"10.1016/j.suscom.2025.101203","url":null,"abstract":"<div><div>The Internet of Things (IoT) plays a vital role in the recent healthcare industry by providing precise diagnostic and treatment capabilities. There is a growing interest in medical IoT incorporated into healthcare systems. The processing unit of all medical IoT comprises complementary metal-oxide semiconductor (CMOS) technology. However, CMOS Medical IoT technology has become integrated into biomedical hardware systems at the nanoscale regime. Due to regulatory, ethical, and technological challenges, including slow processing speeds, high power consumption, and slow switching frequencies, particularly in the gigahertz (GHz) range. On the other hand, compared to traditional computers, quantum technology will accelerate processing by an order of magnitude and affect all artificial and medical (AI) and medical IoT processing applications. Quantum-dot cellular automata (QCA) present a promising alternative digital hardware system in medical IoT. QCA technology makes an optimal choice for establishing circuit design frameworks for AI in medical IoT applications, where low-cost, real-time, energy-efficient performance is crucial. Moreever, encryption and decryption circuits have been used in medical IoT operations to protect sensitive patient data while it is being transmitted and stored. The essential arithmetic and logic unit (ALU) is proposed in this context, which is the foundation for processing and computational units for medical IoT systems at the nanoscale devices. A systematic approach is involved in integrating adders, multiplexers, an ALU, and a logic unit to enhance processor drive and privacy via encryption and decryption in medical IoT. The experimental outcomes reveal that the recommended design overtakes the previous design by 15.48 % in terms of cells and 16.07 % in terms of area. The designs are accurately simulated using the QCADesigner-E 2.0.3 software tool.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101203"},"PeriodicalIF":5.7,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145060099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-09-03DOI: 10.1016/j.suscom.2025.101197
SeyedJalal SeyedShenava, Peyman Zare, Amir Mohammadian
The evolving architecture of rich-renewable Eco-Industrial Park Microgrids (EIP-MGs) introduces significant frequency stability challenges due to the intermittent nature and low inertia of integrated renewable energy sources. To address these limitations, advanced energy storage systems, comprising fixed and mobile electric energy storage systems, have been adopted. Among them, mobile EV energy storage, particularly in the context of e-shared mobility, offers a flexible and scalable solution for load frequency control in modern EIP-MGs. This study presents a novel framework for sustainable transient frequency management using a fractional-order computing-based hybrid cascade controller, TFOID–3DOF–TID (Tilted Fractional-Order Integral and Derivative with Three Degrees of Freedom), optimized via the Crested Porcupine Optimizer (CPO). The proposed control scheme is validated through six case studies under three industrial load disturbance scenarios, with emphasis on transient stability and real-world uncertainties. The evaluations are structured around frequency-domain design criteria based on integral error metrics, including squared and absolute formulationsaimed at analyzing efficiency, sensitivity, adaptability, robustness, stability, and computational burden. The proposed control scheme, featuring the TFOID and 3DOF-TID controllers, is evaluated in comparison with validated metaheuristic-based algorithms. Simulation results demonstrate that the CPO-based TFOID–3DOF–TID controller consistently outperforms other schemes, with improvements including a 22 %–48 % reduction in settling time, a 25 %–55 % decrease in undershoot, and a 30 %–60 % reduction in overshoot across varying scenarios. Additionally, Bode plot evaluations confirm superior phase margins and damping characteristics, while robustness margins improve by up to 60 %, affirming the controller’s resilience under non-ideal operational conditions. These findings provide practical insights for policymakers and engineers aiming to enhance the resilience and sustainability of future-ready industrial microgrids.
{"title":"Sustainable transient frequency management in eco-industrial park microgrids considering e-shared mobility storage using efficient fractional-order computing","authors":"SeyedJalal SeyedShenava, Peyman Zare, Amir Mohammadian","doi":"10.1016/j.suscom.2025.101197","DOIUrl":"10.1016/j.suscom.2025.101197","url":null,"abstract":"<div><div>The evolving architecture of rich-renewable Eco-Industrial Park Microgrids (EIP-MGs) introduces significant frequency stability challenges due to the intermittent nature and low inertia of integrated renewable energy sources. To address these limitations, advanced energy storage systems, comprising fixed and mobile electric energy storage systems, have been adopted. Among them, mobile EV energy storage, particularly in the context of e-shared mobility, offers a flexible and scalable solution for load frequency control in modern EIP-MGs. This study presents a novel framework for sustainable transient frequency management using a fractional-order computing-based hybrid cascade controller, TFOID–3DOF–TID (Tilted Fractional-Order Integral and Derivative with Three Degrees of Freedom), optimized via the Crested Porcupine Optimizer (CPO). The proposed control scheme is validated through six case studies under three industrial load disturbance scenarios, with emphasis on transient stability and real-world uncertainties. The evaluations are structured around frequency-domain design criteria based on integral error metrics, including squared and absolute formulationsaimed at analyzing efficiency, sensitivity, adaptability, robustness, stability, and computational burden. The proposed control scheme, featuring the TFOID and 3DOF-TID controllers, is evaluated in comparison with validated metaheuristic-based algorithms. Simulation results demonstrate that the CPO-based TFOID–3DOF–TID controller consistently outperforms other schemes, with improvements including a 22 %–48 % reduction in settling time, a 25 %–55 % decrease in undershoot, and a 30 %–60 % reduction in overshoot across varying scenarios. Additionally, Bode plot evaluations confirm superior phase margins and damping characteristics, while robustness margins improve by up to 60 %, affirming the controller’s resilience under non-ideal operational conditions. These findings provide practical insights for policymakers and engineers aiming to enhance the resilience and sustainability of future-ready industrial microgrids.</div></div>","PeriodicalId":48686,"journal":{"name":"Sustainable Computing-Informatics & Systems","volume":"48 ","pages":"Article 101197"},"PeriodicalIF":5.7,"publicationDate":"2025-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145004814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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